sn-ENS neuron integration
several figures for each batch
source("/Shared_win/projects/RNA_normal/analysis.10x.r")load integrated obj
GEX.seur <- readRDS("../integration_Nb5d/sn10x_WYS.sct_anno.s.rds")
GEX.seur## An object of class Seurat
## 47356 features across 19418 samples within 3 assays
## Active assay: SCT (20434 features, 0 variable features)
## 2 other assays present: RNA, integrated
## 3 dimensional reductions calculated: pca, tsne, umapref.seur <- readRDS("../../20230704_10x_SZJ/analysis_ref/GSE149524.P21.integration_Anno.s.rds")
ref.seur## An object of class Seurat
## 37583 features across 4419 samples within 3 assays
## Active assay: SCT (16365 features, 0 variable features)
## 2 other assays present: RNA, integrated
## 3 dimensional reductions calculated: pca, tsne, umap# define intAnno1/2 colors
color.A1 <- c("#678BB1","#8AB6CE","#3975C1","#669FDF","#4CC1BD",
"#BF7E6B","#D46B35","#F19258","#FF8080",
"#BDAE8D","#BD66C4","#C03778",
"#97BA59","#DFAB16","#2BA956","#9FE727")
color.A2 <- c("#678BB1","#8AB6CE","#3975C1","#669FDF","#4CC1BD",
"#BF7E6B","#D46B35","#F19258","#FF8080",
"#BDAE8D","#BD66C4","#C03778",
"#97BA59","#C4D116", "#DFAB16","#EDE25A", "#2BA956","#9FE727")
# define batch/condition colors
color.cnt <- scales::hue_pal()(4)[c(2,1,4,3)]
color.test1 <- color.cnt[1:2]
color.test2 <- color.cnt[3:4]
## define feature colors
# Cell2020
material.heat = function(n){
colorRampPalette(
c(
"#283593", # indigo 800
"#3F51B5", # indigo
"#2196F3", # blue
"#00BCD4", # cyan
"#4CAF50", # green
"#8BC34A", # light green
"#CDDC39", # lime
"#FFEB3B", # yellow
"#FFC107", # amber
"#FF9800", # orange
"#FF5722" # deep orange
)
)(n)
}
# Immunity2019, na gray
colors.Immunity <-c("#191970","#121285","#0C0C9A","#0707B0","#0101C5","#0014CF","#0033D3","#0053D8","#0072DD","#0092E1","#00B2E6",
"#00D1EB","#23E8CD","#7AF17B","#D2FA29","#FFEB00","#FFC300","#FF9B00","#FF8400","#FF7800","#FF6B00","#FF5F00","#FF5300",
"#FF4700","#F73B00","#EF2E00","#E62300","#DD1700","#D50B00","#CD0000")
# NatNeur2021, sc-neurons
color.ref <- c("#8AB6CE","#678BB1","#3975C1","#4CC1BD",
"#C03778","#97BA59","#DFAB16","#BF7E6B",
"#D46B35","#BDAE8D","#BD66C4","#2BA956",
"#FF8080","#FF8080","#FF8080","#FF0000")write.table(color.A1, "figures.integration/color.A1.txt", col.names = F, row.names = F, quote = F)
write.table(color.A2, "figures.integration/color.A2.txt", col.names = F, row.names = F, quote = F)
write.table(color.cnt, "figures.integration/color.condition.txt", col.names = F, row.names = F, quote = F)scales::show_col(color.A1)
scales::show_col(color.A2)
scales::show_col(color.cnt)
cowplot::plot_grid(
DimPlot(GEX.seur, reduction = "umap", group.by = "intAnno1", label = T, label.size = 3.25,repel = F, pt.size = 0.05,
cols = color.A1),
DimPlot(GEX.seur, label = F, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "cnt",
cols = color.cnt) ,
rel_widths = c(4.8,5),
ncol = 2)
cowplot::plot_grid(
DimPlot(GEX.seur, reduction = "umap", group.by = "intAnno2", label = T, label.size = 2.65,repel = F, pt.size = 0.05,
cols = color.A2),
DimPlot(GEX.seur, label = F, pt.size = 0.05, repel = F, reduction = 'umap', group.by = "cnt",
cols = color.cnt) ,
rel_widths = c(4.85,5),
ncol = 2)
DimPlot(ref.seur, reduction = "umap", label = T, group.by = "Anno1", cols = color.ref) +
DimPlot(ref.seur, reduction = "umap", label = T, group.by = "Anno2")
PBS_INF
test1.seur <- subset(GEX.seur, subset= cnt %in% c("Nb5d.PBS","Nb5d.INF"))
test1.seur## An object of class Seurat
## 47356 features across 8232 samples within 3 assays
## Active assay: SCT (20434 features, 0 variable features)
## 2 other assays present: RNA, integrated
## 3 dimensional reductions calculated: pca, tsne, umaptest1.seur <- ScaleData(test1.seur, features = rownames(test1.seur))## Centering and scaling data matrixcowplot::plot_grid(
DimPlot(test1.seur, reduction = "umap", group.by = "intAnno1", label = T, label.size = 3.25,repel = F, pt.size = 0.15,
cols = color.A1),
DimPlot(test1.seur, label = F, pt.size = 0.15, repel = F, reduction = 'umap', group.by = "cnt",
cols = color.test1) ,
rel_widths = c(4.8,5),
ncol = 2)
cowplot::plot_grid(
DimPlot(test1.seur, reduction = "umap", group.by = "intAnno2", label = T, label.size = 2.65,repel = F, pt.size = 0.15,
cols = color.A2),
DimPlot(test1.seur, label = F, pt.size = 0.15, repel = F, reduction = 'umap', group.by = "cnt",
cols = color.test1) ,
rel_widths = c(4.85,5),
ncol = 2)
CTL_CKO
test2.seur <- subset(GEX.seur, subset= cnt %in% c("Nb5d.CTL","Nb5d.CKO"))
test2.seur## An object of class Seurat
## 47356 features across 11186 samples within 3 assays
## Active assay: SCT (20434 features, 0 variable features)
## 2 other assays present: RNA, integrated
## 3 dimensional reductions calculated: pca, tsne, umaptest2.seur <- ScaleData(test2.seur, features = rownames(test2.seur))## Centering and scaling data matrixcowplot::plot_grid(
DimPlot(test2.seur, reduction = "umap", group.by = "intAnno1", label = T, label.size = 3.25,repel = F, pt.size = 0.15,
cols = color.A1),
DimPlot(test2.seur, label = F, pt.size = 0.15, repel = F, reduction = 'umap', group.by = "cnt",
cols = color.test2) ,
rel_widths = c(4.8,5),
ncol = 2)
cowplot::plot_grid(
DimPlot(test2.seur, reduction = "umap", group.by = "intAnno2", label = T, label.size = 2.65,repel = F, pt.size = 0.15,
cols = color.A2),
DimPlot(test2.seur, label = F, pt.size = 0.15, repel = F, reduction = 'umap', group.by = "cnt",
cols = color.test2) ,
rel_widths = c(4.85,5),
ncol = 2)
composition
(processed in s1)
final markers
long
(pass)
short_old
markers.old.s <- list(EMN=c("Chat","Bnc2",#"Tox","Ptprt",
"Gfra2","Oprk1",#"Adamtsl1",
"Fbxw15","Fbxw24",#"Chrna7",
"Satb1","Cntnap5b",
"Gabrb1","Nxph1","Lama2","Lrrc7",
"Ryr3",#"Eda",
"Tac1",
#"Kctd8","Ntrk2",
"Penk",
"Fut9","Nfatc1","Egfr",#"Mgll",
"Chrm3"
),
IMN=c("Nos1","Kcnab1",
"Gfra1","Etv1",
#"Man1a","Airn",
"Adcy2","Cmah","Creb5","Vip","Pde1a",
"Ebf1"#,"Gpc5"
),
IN=c("Npas3","Synpr","St18","Gal",
"Neurod6",
#"Kcnk13",
"Moxd1","Sctr",
"Piezo1","Sst",#"Adamts9",
"Kcnn2"),
IPAN=c("Calb2","Calcb","Nmu","Adgrg6",#"Pcdh10",
"Ngfr","Galr1","Il7",#"Aff2",
#"Gpr149",
"Cdh6","Cdh8",
"Clstn2",#"Ano2","Ntrk3",
"Cpne4",#"Vwc2l",
"Cdh9","Scgn",
#"Vcan",
"Cck","Piezo2","Kcnh7",
#"Rerg",
"Bmpr1b","Skap1","Ntng1",
"Tafa2","Nxph2"))pn.intAnno1.test0a <- DotPlot(GEX.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="All intAnno1")
pn.intAnno1.test0a
pn.intAnno2.test0a <- DotPlot(GEX.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="All intAnno2")
pn.intAnno2.test0a
pn.intAnno1.test0b <- DotPlot(GEX.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="All intAnno1")
pn.intAnno1.test0b
pn.intAnno2.test0b <- DotPlot(GEX.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="All intAnno2")
pn.intAnno2.test0b
pn.intAnno1.test1a <- DotPlot(test1.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="PBSvsINF intAnno1")
pn.intAnno1.test1a
pn.intAnno2.test1a <- DotPlot(test1.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="PBSvsINF intAnno2")
pn.intAnno2.test1a
pn.intAnno1.test1b <- DotPlot(test1.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="PBSvsINF intAnno1")
pn.intAnno1.test1b
pn.intAnno2.test1b <- DotPlot(test1.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="PBSvsINF intAnno2")
pn.intAnno2.test1b
pn.intAnno1.test2a <- DotPlot(test2.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="CTLvsCKO intAnno1")
pn.intAnno1.test2a
pn.intAnno2.test2a <- DotPlot(test2.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="CTLvsCKO intAnno2")
pn.intAnno2.test2a
pn.intAnno1.test2b <- DotPlot(test2.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="CTLvsCKO intAnno1")
pn.intAnno1.test2b
pn.intAnno2.test2b <- DotPlot(test2.seur, features = as.vector(unlist(markers.old.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="CTLvsCKO intAnno2")
pn.intAnno2.test2b
pn.ref.a <- DotPlot(ref.seur, features = as.vector(unlist(markers.old.s)), group.by = "Anno2",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="NatNeur2021 P21")
pn.ref.a
pn.ref.b <- DotPlot(ref.seur, features = as.vector(unlist(markers.old.s)), group.by = "Anno2",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="NatNeur2021 P21")
pn.ref.b
short_new
markers.new.s <- list(EMN=c("Chat","Bnc2",#"Tox","Ptprt",
"Gfra2","Oprk1",#"Adamtsl1",
"Fbxw15","Fbxw24",#"Chrna7",
"Satb1","Itga6","Cntnap5b",
"Chgb","Nxph1",
"Lama2","Efnb2","Itgb8",
"Lrrc7",
"Ryr3",#"Eda",
"Tac1",
#"Kctd8","Ntrk2",
"Penk",
"Fut9","Nfatc1","Egfr",#"Mgll",
"Chrm3"
),
IMN=c("Nos1","Kcnab1",
"Gfra1","Etv1",
#"Man1a","Airn",
"Adcy2","Cmah","Col25a1",
"Mid1","Creb5","Vip","Pde1a",
"Ebf1",#,"Gpc5"
"Ppara","Pcdh11x",
"Adcy8","Grp"
),
IN=c("Npas3","Synpr","St18","Gal",
"Cdh10","Neurod6",
"Kcnk13",
"Moxd1","Sctr",
"Piezo1","Vipr2","Sst",#"Adamts9",
"Kcnn2"
),
IPAN=c("Calb2","Adcy1",
"Nmu","Adgrg6",#"Pcdh10",
"Ngfr","Il7",
"Itgb6","Calcb","Galr1",
#"Aff2",
#"Gpr149",
"Met",
"Cpne4","Cdh6","Cdh8",
"Clstn2",#"Ano2","Ntrk3",
#"Vwc2l",
"Car10","Scgn","Glp2r","Cck",
"Cdh9",
#"Vcan",
"Dcc",
"Gabrb1",
"Piezo2","Kcnh7",
#"Rerg",
"Bmpr1b","Ntng1","Skap1",
"Tafa2","Nxph2"))pm.intAnno1.test0a <- DotPlot(GEX.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="All intAnno1")
pm.intAnno1.test0a
pm.intAnno2.test0a <- DotPlot(GEX.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="All intAnno2")
pm.intAnno2.test0a
pm.intAnno1.test0b <- DotPlot(GEX.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="All intAnno1")
pm.intAnno1.test0b
pm.intAnno2.test0b <- DotPlot(GEX.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="All intAnno2")
pm.intAnno2.test0b
pm.intAnno1.test1a <- DotPlot(test1.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="PBSvsINF intAnno1")
pm.intAnno1.test1a
pm.intAnno2.test1a <- DotPlot(test1.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="PBSvsINF intAnno2")
pm.intAnno2.test1a
pm.intAnno1.test1b <- DotPlot(test1.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="PBSvsINF intAnno1")
pm.intAnno1.test1b
pm.intAnno2.test1b <- DotPlot(test1.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="PBSvsINF intAnno2")
pm.intAnno2.test1b
pm.intAnno1.test2a <- DotPlot(test2.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="CTLvsCKO intAnno1")
pm.intAnno1.test2a
pm.intAnno2.test2a <- DotPlot(test2.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="CTLvsCKO intAnno2")
pm.intAnno2.test2a
pm.intAnno1.test2b <- DotPlot(test2.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="CTLvsCKO intAnno1")
pm.intAnno1.test2b
pm.intAnno2.test2b <- DotPlot(test2.seur, features = as.vector(unlist(markers.new.s)), group.by = "intAnno2",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="CTLvsCKO intAnno2")
pm.intAnno2.test2b
pm.ref.a <- DotPlot(ref.seur, features = as.vector(unlist(markers.new.s)), group.by = "Anno2",
cols = c("midnightblue","darkorange1")) +
#coord_flip() +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) +
scale_y_discrete(limits=rev) +
labs(title="NatNeur2021 P21")
pm.ref.a
pm.ref.b <- DotPlot(ref.seur, features = as.vector(unlist(markers.new.s)), group.by = "Anno2",
cols = c("midnightblue","darkorange1")) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.4,size = 9.15))+
scale_color_gradientn(colours = material.heat(100)) +
#scale_x_discrete(limits=rev) + scale_y_discrete(limits=rev) +
labs(title="NatNeur2021 P21")
pm.ref.b
plot0727
repeat plot in 20230727
abreast violin plot to show markers: Il13ra1, Il4ra, Calca, Calcb,
Nmu, Chat
modified violins and dots
PBS, INF separated
add wiocox.test in each intAnno
head(test1.seur@meta.data)## orig.ident nCount_RNA nFeature_RNA percent.mt percent.rb
## AAACCCACAAGACGAC-1_1 Nb5d.PBS_INF 3257 1801 0.36843721 0.3991403
## AAACCCAGTGGGCTCT-1_1 Nb5d.PBS_INF 1511 997 0.66181337 0.4632694
## AAACCCAGTTTGTTCT-1_1 Nb5d.PBS_INF 2855 1577 0.98073555 0.3152364
## AAACCCATCCTAGCCT-1_1 Nb5d.PBS_INF 2433 1451 0.08220304 0.3699137
## AAACCCATCGAAACAA-1_1 Nb5d.PBS_INF 3129 1656 0.12783637 0.4474273
## AAACCCATCGGTCAGC-1_1 Nb5d.PBS_INF 2201 1294 0.22716947 0.2271695
## S.Score G2M.Score Phase cnt rep newAnno
## AAACCCACAAGACGAC-1_1 0.011590405 -0.0004169865 S Nb5d.INF rep4 EMN3
## AAACCCAGTGGGCTCT-1_1 -0.024203070 0.0012414826 G2M Nb5d.PBS rep4 IPAN1
## AAACCCAGTTTGTTCT-1_1 -0.013980476 0.0039329410 G2M Nb5d.INF rep1 EMN3
## AAACCCATCCTAGCCT-1_1 -0.028925620 -0.0132582758 G1 Nb5d.INF rep2 EMN1
## AAACCCATCGAAACAA-1_1 -0.008077289 -0.0028336129 G1 Nb5d.PBS rep3 IPAN4
## AAACCCATCGGTCAGC-1_1 -0.023612751 0.0327239644 G2M Nb5d.PBS rep4 EMN1
## sample tissue nCount_SCT nFeature_SCT condition
## AAACCCACAAGACGAC-1_1 Nb5d.INF4 Ileum 2592 1794 INF_CTL
## AAACCCAGTGGGCTCT-1_1 Nb5d.PBS4 Ileum 1694 996 PBS_CTL
## AAACCCAGTTTGTTCT-1_1 Nb5d.INF1 Ileum 2495 1576 INF_CTL
## AAACCCATCCTAGCCT-1_1 Nb5d.INF2 Ileum 2324 1451 INF_CTL
## AAACCCATCGAAACAA-1_1 Nb5d.PBS3 Ileum 2552 1646 PBS_CTL
## AAACCCATCGGTCAGC-1_1 Nb5d.PBS4 Ileum 2171 1293 PBS_CTL
## seurat_clusters sort_clusters intAnno1 intAnno2
## AAACCCACAAGACGAC-1_1 11 11 EMN2 EMN2
## AAACCCAGTGGGCTCT-1_1 22 22 IPAN1 IPAN1.1
## AAACCCAGTTTGTTCT-1_1 11 11 EMN2 EMN2
## AAACCCATCCTAGCCT-1_1 4 4 EMN1 EMN1
## AAACCCATCGAAACAA-1_1 19 19 IPAN4 IPAN4
## AAACCCATCGGTCAGC-1_1 8 8 EMN1 EMN1
## score.EMN1 score.EMN2 score.EMN3 score.EMN4
## AAACCCACAAGACGAC-1_1 0.07919591 0.241706810 0.27217296 0.12854583
## AAACCCAGTGGGCTCT-1_1 -0.15104916 -0.182227557 -0.08972356 -0.02780619
## AAACCCAGTTTGTTCT-1_1 0.06398507 0.271974508 0.38593823 0.13952419
## AAACCCATCCTAGCCT-1_1 0.45628820 0.004121058 -0.09053160 -0.25144656
## AAACCCATCGAAACAA-1_1 -0.22677892 -0.176042364 0.17667288 0.07109063
## AAACCCATCGGTCAGC-1_1 0.41500886 0.078972206 -0.04352445 -0.01717643
## score.EMN5 score.IMN1 score.IMN2 score.IMN3
## AAACCCACAAGACGAC-1_1 0.112776596 -0.048743641 0.087677011 -0.06945631
## AAACCCAGTGGGCTCT-1_1 -0.078949105 -0.164458377 -0.010275168 0.03123894
## AAACCCAGTTTGTTCT-1_1 0.076261976 0.013262972 -0.086306052 -0.16199490
## AAACCCATCCTAGCCT-1_1 -0.014058236 -0.106028650 -0.055285442 -0.12949849
## AAACCCATCGAAACAA-1_1 0.102718840 -0.004683565 -0.006606094 -0.07936345
## AAACCCATCGGTCAGC-1_1 -0.008261381 -0.105501039 0.053248882 -0.05854380
## score.IMN4 score.IN1 score.IN2 score.IN3
## AAACCCACAAGACGAC-1_1 0.002799472 -0.052584879 -0.04337769 0.02522416
## AAACCCAGTGGGCTCT-1_1 -0.082036820 -0.107881694 -0.07353192 0.06210550
## AAACCCAGTTTGTTCT-1_1 -0.030034210 -0.109808107 -0.05886169 0.03389016
## AAACCCATCCTAGCCT-1_1 -0.079803316 -0.135613705 -0.12109194 0.16525651
## AAACCCATCGAAACAA-1_1 -0.014348463 -0.053893573 0.11275158 -0.04386948
## AAACCCATCGGTCAGC-1_1 -0.066043337 -0.004224746 0.01935024 0.07307944
## score.IPAN1.1 score.IPAN1.2 score.IPAN2.1 score.IPAN2.2
## AAACCCACAAGACGAC-1_1 -0.06921930 -0.05854091 -0.12090052 -0.03200085
## AAACCCAGTGGGCTCT-1_1 0.39874813 0.50626549 0.08046528 -0.05788911
## AAACCCAGTTTGTTCT-1_1 -0.10141645 -0.04456315 -0.04661481 0.01507260
## AAACCCATCCTAGCCT-1_1 -0.02942262 -0.11171721 -0.08567541 -0.03353428
## AAACCCATCGAAACAA-1_1 0.01651234 -0.01855972 0.11727520 0.26751667
## AAACCCATCGGTCAGC-1_1 -0.08079498 -0.09590834 -0.06355259 0.07461285
## score.IPAN3 score.IPAN4 score.INFxCTL_IPAN1
## AAACCCACAAGACGAC-1_1 0.009074399 -0.033702006 0.02559085
## AAACCCAGTGGGCTCT-1_1 0.075643417 -0.066791575 0.10998073
## AAACCCAGTTTGTTCT-1_1 0.023826742 0.025015471 -0.01209398
## AAACCCATCCTAGCCT-1_1 0.011699673 -0.003267128 0.03061715
## AAACCCATCGAAACAA-1_1 0.161399262 0.714055897 -0.02355308
## AAACCCATCGGTCAGC-1_1 -0.100991813 0.072239711 0.03672466
## score.INFxCTL_IPAN2 score.All_PBSup score.All_INFup
## AAACCCACAAGACGAC-1_1 0.03219975 0.038754467 0.14092349
## AAACCCAGTGGGCTCT-1_1 0.01085557 0.141496754 0.15347058
## AAACCCAGTTTGTTCT-1_1 0.07094068 0.042089530 0.11806283
## AAACCCATCCTAGCCT-1_1 -0.10919176 0.089116669 0.07064033
## AAACCCATCGAAACAA-1_1 0.10012150 -0.068982944 0.09493913
## AAACCCATCGGTCAGC-1_1 -0.13022208 0.005274842 0.09648734
## score.All_CTLup score.All_CKOup score.IPAN1_PBSup
## AAACCCACAAGACGAC-1_1 0.04699572 0.12328759 -0.07772672
## AAACCCAGTGGGCTCT-1_1 0.16455231 0.27409426 0.83477666
## AAACCCAGTTTGTTCT-1_1 0.02675111 0.04651514 -0.04362891
## AAACCCATCCTAGCCT-1_1 -0.06082137 0.17571025 -0.02777775
## AAACCCATCGAAACAA-1_1 -0.06550854 -0.04899024 0.16010606
## AAACCCATCGGTCAGC-1_1 -0.14775899 0.24252663 0.05487237
## score.IPAN1_INFup score.IPAN1_CTLup score.IPAN1_CKOup
## AAACCCACAAGACGAC-1_1 0.09536479 0.005898024 -0.08990901
## AAACCCAGTGGGCTCT-1_1 0.09832854 0.142199961 0.95544282
## AAACCCAGTTTGTTCT-1_1 0.05657979 -0.036408520 -0.07090544
## AAACCCATCCTAGCCT-1_1 0.02614203 0.014353383 -0.20984452
## AAACCCATCGAAACAA-1_1 0.04713297 -0.020370635 0.21988923
## AAACCCATCGGTCAGC-1_1 0.09156870 0.008539614 -0.00446080
## score.IPAN2_PBSup score.IPAN2_INFup score.IPAN2_CTLup
## AAACCCACAAGACGAC-1_1 0.23986616 0.09934860 0.03204373
## AAACCCAGTGGGCTCT-1_1 0.46980201 -0.02241394 0.03228071
## AAACCCAGTTTGTTCT-1_1 0.18824079 0.10546492 0.07251113
## AAACCCATCCTAGCCT-1_1 -0.12377840 -0.04855095 -0.09076901
## AAACCCATCGAAACAA-1_1 0.19893775 0.05686627 0.05049066
## AAACCCATCGGTCAGC-1_1 0.07972086 -0.01931504 -0.13585336
## score.IPAN2_CKOup score.IEGs
## AAACCCACAAGACGAC-1_1 -0.30729036 0.13012508
## AAACCCAGTGGGCTCT-1_1 0.58710490 0.01972603
## AAACCCAGTTTGTTCT-1_1 -0.29511538 -0.01351550
## AAACCCATCCTAGCCT-1_1 -0.43075122 -0.02168908
## AAACCCATCGAAACAA-1_1 -0.14695600 0.01547855
## AAACCCATCGGTCAGC-1_1 -0.02570226 -0.01439351mod metadata
cnt1
# cnt1 as PBS/INF
test1.seur$cnt1 <- as.character(test1.seur$cnt)
test1.seur$cnt1 <- gsub("Nb5d.","",test1.seur$cnt1)
test1.seur$cnt1 <- factor(test1.seur$cnt1,
levels = c("PBS","INF"))
head(test1.seur$cnt1)## AAACCCACAAGACGAC-1_1 AAACCCAGTGGGCTCT-1_1 AAACCCAGTTTGTTCT-1_1
## INF PBS INF
## AAACCCATCCTAGCCT-1_1 AAACCCATCGAAACAA-1_1 AAACCCATCGGTCAGC-1_1
## INF PBS PBS
## Levels: PBS INFcnt2
levels(test1.seur$intAnno1)## [1] "EMN1" "EMN2" "EMN3" "EMN4" "EMN5" "IMN1" "IMN2" "IMN3" "IMN4"
## [10] "IN1" "IN2" "IN3" "IPAN1" "IPAN2" "IPAN3" "IPAN4"level.cnt2 <- as.vector(sapply(levels(test1.seur$intAnno1),function(x){
paste0(x,c(".PBS",".INF"))
}))
level.cnt2## [1] "EMN1.PBS" "EMN1.INF" "EMN2.PBS" "EMN2.INF" "EMN3.PBS" "EMN3.INF"
## [7] "EMN4.PBS" "EMN4.INF" "EMN5.PBS" "EMN5.INF" "IMN1.PBS" "IMN1.INF"
## [13] "IMN2.PBS" "IMN2.INF" "IMN3.PBS" "IMN3.INF" "IMN4.PBS" "IMN4.INF"
## [19] "IN1.PBS" "IN1.INF" "IN2.PBS" "IN2.INF" "IN3.PBS" "IN3.INF"
## [25] "IPAN1.PBS" "IPAN1.INF" "IPAN2.PBS" "IPAN2.INF" "IPAN3.PBS" "IPAN3.INF"
## [31] "IPAN4.PBS" "IPAN4.INF"# for violin comparison
list.cnt2 <- lapply(levels(test1.seur$intAnno1),function(x){
paste0(x,c(".PBS",".INF"))
})
list.cnt2## [[1]]
## [1] "EMN1.PBS" "EMN1.INF"
##
## [[2]]
## [1] "EMN2.PBS" "EMN2.INF"
##
## [[3]]
## [1] "EMN3.PBS" "EMN3.INF"
##
## [[4]]
## [1] "EMN4.PBS" "EMN4.INF"
##
## [[5]]
## [1] "EMN5.PBS" "EMN5.INF"
##
## [[6]]
## [1] "IMN1.PBS" "IMN1.INF"
##
## [[7]]
## [1] "IMN2.PBS" "IMN2.INF"
##
## [[8]]
## [1] "IMN3.PBS" "IMN3.INF"
##
## [[9]]
## [1] "IMN4.PBS" "IMN4.INF"
##
## [[10]]
## [1] "IN1.PBS" "IN1.INF"
##
## [[11]]
## [1] "IN2.PBS" "IN2.INF"
##
## [[12]]
## [1] "IN3.PBS" "IN3.INF"
##
## [[13]]
## [1] "IPAN1.PBS" "IPAN1.INF"
##
## [[14]]
## [1] "IPAN2.PBS" "IPAN2.INF"
##
## [[15]]
## [1] "IPAN3.PBS" "IPAN3.INF"
##
## [[16]]
## [1] "IPAN4.PBS" "IPAN4.INF"# cnt2 as intAnno1.PBS/INF
test1.seur$cnt2 <- factor(paste0(as.character(test1.seur$intAnno1),
".",
as.character(test1.seur$cnt1)),
levels = level.cnt2)head(test1.seur$cnt2)## AAACCCACAAGACGAC-1_1 AAACCCAGTGGGCTCT-1_1 AAACCCAGTTTGTTCT-1_1
## EMN2.INF IPAN1.PBS EMN2.INF
## AAACCCATCCTAGCCT-1_1 AAACCCATCGAAACAA-1_1 AAACCCATCGGTCAGC-1_1
## EMN1.INF IPAN4.PBS EMN1.PBS
## 32 Levels: EMN1.PBS EMN1.INF EMN2.PBS EMN2.INF EMN3.PBS EMN3.INF ... IPAN4.INFDEGs
# DEGs
df_test1.DEGs_new <- read.csv("../integration_Nb5d/Baf53cre_Nb.DEGs.PBSvsINF.intAnno2.csv")
df_test1.DEGs_new$cluster <- factor(df_test1.DEGs_new$cluster,
levels = c("Nb5d.PBS","Nb5d.INF"))
head(df_test1.DEGs_new)## X p_val avg_log2FC pct.1 pct.2 p_val_adj cluster
## 1 Ctnna3 8.684419e-51 0.4588045 0.846 0.761 2.129854e-46 Nb5d.PBS
## 2 Malat1 1.335552e-36 0.1410444 1.000 1.000 3.275441e-32 Nb5d.PBS
## 3 AY036118 7.553267e-24 0.2576808 0.788 0.750 1.852439e-19 Nb5d.PBS
## 4 Fgfr2 2.040025e-21 0.2291577 0.838 0.769 5.003161e-17 Nb5d.PBS
## 5 4930447N08Rik 2.483724e-18 0.2687259 0.726 0.659 6.091333e-14 Nb5d.PBS
## 6 Prkn 2.213112e-17 0.2764906 0.516 0.421 5.427657e-13 Nb5d.PBS
## gene intAnno2
## 1 Ctnna3 All
## 2 Malat1 All
## 3 AY036118 All
## 4 Fgfr2 All
## 5 4930447N08Rik All
## 6 Prkn Allnames_new <- unique(df_test1.DEGs_new$intAnno2)
names_new## [1] "All" "EMNs" "IMNs" "IPAN1" "IPAN2" "EMN1" "EMN2"
## [8] "EMN3" "EMN4" "EMN5" "IMN1" "IMN2" "IMN3" "IMN4"
## [15] "IN1" "IN2" "IN3" "IPAN1.1" "IPAN1.2" "IPAN2.1" "IPAN2.2"
## [22] "IPAN3" "IPAN4"options("max.print")## $max.print
## [1] 99999## cut1
cut.padj = 0.05
cut.log2FC = 0.3
cut.pct1 = 0.1
df_test1.DEGs_new %>% filter(p_val_adj < cut.padj &
abs(avg_log2FC) > cut.log2FC &
pct.1 > cut.pct1) %>%
group_by(intAnno2,cluster) %>% filter ( intAnno2 == "IPAN1") %>% as.data.frame()## X p_val avg_log2FC pct.1 pct.2 p_val_adj cluster
## 1 Zfp804a1 2.807963e-43 0.9178380 0.997 0.983 6.886529e-39 Nb5d.PBS
## 2 Fgf131 2.599880e-39 0.8256163 1.000 0.993 6.376205e-35 Nb5d.PBS
## 3 Gm152611 1.174558e-27 1.2851620 0.865 0.583 2.880603e-23 Nb5d.PBS
## 4 Cdh61 2.345303e-27 0.8959969 0.926 0.806 5.751856e-23 Nb5d.PBS
## 5 Tafa11 3.531556e-21 0.5419270 1.000 0.986 8.661142e-17 Nb5d.PBS
## 6 Luzp21 8.214889e-20 1.2231915 0.623 0.288 2.014702e-15 Nb5d.PBS
## 7 Ppp3ca 3.160521e-18 0.5540905 0.968 0.934 7.751177e-14 Nb5d.PBS
## 8 Gm156801 3.777871e-16 1.1592365 0.480 0.194 9.265228e-12 Nb5d.PBS
## 9 Efr3a1 4.417514e-16 0.6065075 0.929 0.830 1.083395e-11 Nb5d.PBS
## 10 Lsamp 3.524202e-15 0.4351957 1.000 0.997 8.643106e-11 Nb5d.PBS
## 11 Arhgap61 4.007052e-13 0.9236485 0.628 0.392 9.827294e-09 Nb5d.PBS
## 12 Ano21 4.206697e-13 0.5683202 0.913 0.882 1.031692e-08 Nb5d.PBS
## 13 Filip11 8.818976e-13 0.5644433 0.945 0.844 2.162854e-08 Nb5d.PBS
## 14 Otof 1.540171e-12 0.9306861 0.393 0.142 3.777270e-08 Nb5d.PBS
## 15 Rab3c1 3.526455e-12 0.4683026 0.960 0.910 8.648632e-08 Nb5d.PBS
## 16 Rbfox11 1.360631e-10 0.3720548 1.000 0.997 3.336948e-06 Nb5d.PBS
## 17 Adgrg61 3.451285e-10 0.6935988 0.834 0.753 8.464277e-06 Nb5d.PBS
## 18 Ndufs12 5.810237e-10 0.8387902 0.404 0.188 1.424961e-05 Nb5d.PBS
## 19 Gpr851 5.990973e-10 0.7349275 0.488 0.260 1.469286e-05 Nb5d.PBS
## 20 Pdzrn41 1.691186e-09 0.6758964 0.757 0.625 4.147634e-05 Nb5d.PBS
## 21 Kctd121 3.142396e-09 0.6097306 0.256 0.076 7.706726e-05 Nb5d.PBS
## 22 Prom11 1.004221e-08 0.7204048 0.330 0.135 2.462852e-04 Nb5d.PBS
## 23 Gpr1491 1.440566e-08 0.5346354 0.823 0.736 3.532987e-04 Nb5d.PBS
## 24 1810034E14Rik1 1.878015e-08 0.6179254 0.612 0.385 4.605831e-04 Nb5d.PBS
## 25 Cnr1 4.017780e-08 0.4808448 0.868 0.778 9.853606e-04 Nb5d.PBS
## 26 Cntn5 5.943062e-08 0.4834572 1.000 1.000 1.457536e-03 Nb5d.PBS
## 27 Gm16541 6.603472e-08 0.7159641 0.575 0.396 1.619501e-03 Nb5d.PBS
## 28 D030068K23Rik1 9.186788e-08 0.6251514 0.485 0.271 2.253060e-03 Nb5d.PBS
## 29 Fgfr23 9.961633e-08 0.4728528 0.908 0.819 2.443090e-03 Nb5d.PBS
## 30 Gm287502 1.094314e-07 0.6274474 0.435 0.229 2.683804e-03 Nb5d.PBS
## 31 Rnf152 1.878742e-07 0.5153835 0.166 0.038 4.607614e-03 Nb5d.PBS
## 32 Kcnb2 3.121837e-07 0.5564509 0.976 0.979 7.656304e-03 Nb5d.PBS
## 33 Nme6 5.515540e-07 0.5636297 0.179 0.049 1.352686e-02 Nb5d.PBS
## 34 1700010I02Rik 7.585540e-07 0.4789062 0.153 0.035 1.860354e-02 Nb5d.PBS
## 35 Gm471532 8.109604e-07 0.4627286 0.124 0.021 1.988880e-02 Nb5d.PBS
## 36 Gm32335 8.849266e-07 0.6420728 0.530 0.351 2.170282e-02 Nb5d.PBS
## 37 Mef2c2 8.947898e-07 0.6108882 0.206 0.066 2.194472e-02 Nb5d.PBS
## 38 Dapk2 9.079763e-07 0.6353549 0.485 0.312 2.226812e-02 Nb5d.PBS
## 39 1110019D14Rik 1.175473e-06 0.6239660 0.230 0.083 2.882847e-02 Nb5d.PBS
## 40 A330008L17Rik 1.204074e-06 0.6815908 0.269 0.135 2.952990e-02 Nb5d.PBS
## 41 P2rx2 1.645730e-06 0.5339780 0.396 0.212 4.036154e-02 Nb5d.PBS
## 42 Hs3st5 1.920171e-06 0.3604601 0.881 0.823 4.709220e-02 Nb5d.PBS
## 43 Parm12 1.075991e-104 2.1724505 0.931 0.493 2.638869e-100 Nb5d.INF
## 44 Nmu1 2.136217e-82 2.0692761 0.962 0.546 5.239073e-78 Nb5d.INF
## 45 Cadm22 2.247800e-73 2.6501830 0.819 0.285 5.512730e-69 Nb5d.INF
## 46 Sgip11 7.278459e-69 1.3828413 0.965 0.789 1.785042e-64 Nb5d.INF
## 47 App1 1.928396e-66 1.4019565 0.965 0.768 4.729391e-62 Nb5d.INF
## 48 Fam155a3 5.924580e-64 1.1345751 1.000 0.989 1.453003e-59 Nb5d.INF
## 49 Dgki1 8.188198e-60 0.9389057 1.000 0.997 2.008156e-55 Nb5d.INF
## 50 4930432L08Rik1 4.960967e-58 2.0677918 0.847 0.401 1.216677e-53 Nb5d.INF
## 51 Kcnh82 9.483323e-51 1.9055502 0.642 0.137 2.325785e-46 Nb5d.INF
## 52 Gng21 2.747814e-48 1.4543326 0.833 0.533 6.739014e-44 Nb5d.INF
## 53 Cacnb41 1.137876e-43 1.6083705 0.691 0.266 2.790640e-39 Nb5d.INF
## 54 Hnrnpll1 1.164547e-42 1.5964868 0.615 0.179 2.856051e-38 Nb5d.INF
## 55 Col24a11 2.666312e-40 2.6309109 0.385 0.016 6.539131e-36 Nb5d.INF
## 56 Gm268712 3.622215e-40 0.8745574 0.986 0.950 8.883482e-36 Nb5d.INF
## 57 Alk2 2.235025e-34 1.8410770 0.635 0.277 5.481399e-30 Nb5d.INF
## 58 Calcb1 3.010440e-34 1.2126176 0.833 0.615 7.383104e-30 Nb5d.INF
## 59 Asxl31 3.433278e-34 1.3130268 0.639 0.216 8.420115e-30 Nb5d.INF
## 60 Tmeff21 2.592977e-33 1.0129207 0.955 0.934 6.359276e-29 Nb5d.INF
## 61 Nell12 5.890455e-32 0.9233766 0.927 0.807 1.444634e-27 Nb5d.INF
## 62 Pcsk22 1.580598e-31 1.0221099 0.882 0.786 3.876417e-27 Nb5d.INF
## 63 Abi3bp1 6.445359e-29 1.5634638 0.410 0.063 1.580724e-24 Nb5d.INF
## 64 Igf1r1 1.716889e-28 0.9882020 0.917 0.815 4.210671e-24 Nb5d.INF
## 65 Lingo22 2.992058e-28 0.8543086 0.997 0.979 7.338023e-24 Nb5d.INF
## 66 Dpyd 1.594534e-27 0.8040543 0.979 0.887 3.910595e-23 Nb5d.INF
## 67 Tll11 5.407854e-26 1.3664326 0.361 0.047 1.326276e-21 Nb5d.INF
## 68 Negr1 1.503459e-25 0.9415294 0.882 0.728 3.687234e-21 Nb5d.INF
## 69 Dysf1 6.038896e-24 1.3775355 0.583 0.288 1.481039e-19 Nb5d.INF
## 70 Syt91 1.700982e-23 0.9292975 0.826 0.641 4.171659e-19 Nb5d.INF
## 71 Gm300941 2.054852e-23 1.3294833 0.351 0.055 5.039524e-19 Nb5d.INF
## 72 Scn3a1 2.128004e-23 0.8035418 0.906 0.770 5.218930e-19 Nb5d.INF
## 73 Epha71 1.243026e-22 1.3068367 0.469 0.140 3.048520e-18 Nb5d.INF
## 74 Itih5 2.987203e-22 0.9396656 0.240 0.011 7.326115e-18 Nb5d.INF
## 75 Syt1 1.980907e-21 0.5812574 0.990 0.987 4.858173e-17 Nb5d.INF
## 76 Dgkg1 5.824735e-21 0.5318450 0.990 0.982 1.428516e-16 Nb5d.INF
## 77 Ppm1h2 6.613761e-21 0.8237434 0.882 0.707 1.622025e-16 Nb5d.INF
## 78 Ctnnd2 1.919416e-19 0.8719439 0.788 0.541 4.707367e-15 Nb5d.INF
## 79 Galnt131 2.130175e-19 1.0822264 0.708 0.501 5.224254e-15 Nb5d.INF
## 80 Gm12709 3.326302e-19 1.0902550 0.601 0.361 8.157756e-15 Nb5d.INF
## 81 1700111E14Rik1 4.880224e-19 0.7344907 0.934 0.950 1.196875e-14 Nb5d.INF
## 82 Epha6 1.668328e-18 0.9320389 0.795 0.649 4.091574e-14 Nb5d.INF
## 83 Rcan31 3.345314e-18 0.9868467 0.431 0.150 8.204383e-14 Nb5d.INF
## 84 Ptchd4 1.549455e-17 0.7943816 0.188 0.008 3.800038e-13 Nb5d.INF
## 85 Rab27b1 1.699653e-17 1.0190557 0.628 0.467 4.168399e-13 Nb5d.INF
## 86 Gm38405 1.702483e-17 1.4303310 0.191 0.011 4.175340e-13 Nb5d.INF
## 87 Ppp1r12b1 4.474937e-17 0.7289944 0.851 0.683 1.097478e-12 Nb5d.INF
## 88 Kif5a1 1.926266e-16 0.7521534 0.837 0.665 4.724166e-12 Nb5d.INF
## 89 Pak71 3.402264e-16 0.7740531 0.792 0.609 8.344053e-12 Nb5d.INF
## 90 Dner2 4.474026e-16 0.9619404 0.441 0.169 1.097255e-11 Nb5d.INF
## 91 Trhde 5.593460e-16 0.6860635 0.149 0.003 1.371796e-11 Nb5d.INF
## 92 Tmcc32 1.420198e-15 1.0647123 0.444 0.172 3.483036e-11 Nb5d.INF
## 93 Fhl11 1.595311e-15 0.9481742 0.559 0.317 3.912501e-11 Nb5d.INF
## 94 Dclk12 3.483586e-15 0.7298482 0.844 0.710 8.543495e-11 Nb5d.INF
## 95 Gria4 5.262180e-15 0.8870638 0.688 0.470 1.290550e-10 Nb5d.INF
## 96 Grp1 1.017341e-14 0.8228428 0.267 0.050 2.495029e-10 Nb5d.INF
## 97 Gpc6 2.869327e-14 1.4548572 0.615 0.383 7.037024e-10 Nb5d.INF
## 98 Adcy2 3.184482e-14 0.7526644 0.205 0.024 7.809942e-10 Nb5d.INF
## 99 Galm 6.957875e-14 0.7771259 0.205 0.029 1.706419e-09 Nb5d.INF
## 100 Aff23 7.761133e-14 0.5975160 0.875 0.699 1.903418e-09 Nb5d.INF
## 101 Map3k31 8.886742e-14 0.8545849 0.552 0.332 2.179473e-09 Nb5d.INF
## 102 Gm218471 1.445379e-12 0.5821607 0.812 0.551 3.544791e-08 Nb5d.INF
## 103 Rock11 4.300319e-12 0.7451907 0.674 0.475 1.054653e-07 Nb5d.INF
## 104 Apbb21 6.333930e-12 0.7920275 0.524 0.309 1.553396e-07 Nb5d.INF
## 105 Adam121 6.450169e-12 0.8857523 0.420 0.201 1.581904e-07 Nb5d.INF
## 106 Kcnt21 6.869812e-12 0.6137030 0.934 0.894 1.684821e-07 Nb5d.INF
## 107 Kcna4 6.995028e-12 0.7228218 0.215 0.045 1.715531e-07 Nb5d.INF
## 108 Myo3b 8.083859e-12 1.0179703 0.167 0.018 1.982566e-07 Nb5d.INF
## 109 Meg3 9.013449e-12 0.3131038 1.000 1.000 2.210548e-07 Nb5d.INF
## 110 Gm16158 2.216743e-11 0.7599130 0.201 0.040 5.436563e-07 Nb5d.INF
## 111 Bmpr21 2.647548e-11 0.6808265 0.632 0.456 6.493112e-07 Nb5d.INF
## 112 Mgat52 4.044154e-11 0.6689303 0.493 0.343 9.918288e-07 Nb5d.INF
## 113 Slco3a12 4.187152e-11 0.7235636 0.733 0.644 1.026899e-06 Nb5d.INF
## 114 Raph11 4.334610e-11 0.7335959 0.611 0.383 1.063063e-06 Nb5d.INF
## 115 Stxbp5l1 5.360225e-11 0.5304509 0.740 0.673 1.314595e-06 Nb5d.INF
## 116 Large12 5.533504e-11 0.7115061 0.760 0.654 1.357092e-06 Nb5d.INF
## 117 Trpm71 7.170477e-11 0.6973080 0.597 0.361 1.758560e-06 Nb5d.INF
## 118 Antxr21 8.919431e-11 0.8469972 0.392 0.195 2.187491e-06 Nb5d.INF
## 119 Gm269173 1.344304e-10 0.7888886 0.722 0.604 3.296906e-06 Nb5d.INF
## 120 Gm49226 2.864540e-10 0.7784810 0.378 0.169 7.025283e-06 Nb5d.INF
## 121 Atp6v0a11 3.306303e-10 0.5350307 0.865 0.715 8.108707e-06 Nb5d.INF
## 122 Rad51b 5.457911e-10 0.7331041 0.177 0.032 1.338553e-05 Nb5d.INF
## 123 Gucy1a22 6.271109e-10 0.6683144 0.701 0.522 1.537990e-05 Nb5d.INF
## 124 Grm7 1.107330e-09 0.6650001 0.740 0.668 2.715727e-05 Nb5d.INF
## 125 Iqgap21 1.277660e-09 0.5961049 0.667 0.536 3.133460e-05 Nb5d.INF
## 126 9530059O14Rik2 1.362684e-09 0.4010472 0.986 0.974 3.341983e-05 Nb5d.INF
## 127 Fam117a 3.004610e-09 0.7980456 0.326 0.140 7.368806e-05 Nb5d.INF
## 128 Arpp211 3.073181e-09 0.8474036 0.358 0.172 7.536977e-05 Nb5d.INF
## 129 Klf6 4.095137e-09 0.6453736 0.403 0.193 1.004332e-04 Nb5d.INF
## 130 Ubr51 5.958235e-09 0.5659184 0.830 0.697 1.461257e-04 Nb5d.INF
## 131 Nell1os1 6.082172e-09 0.7021925 0.608 0.475 1.491653e-04 Nb5d.INF
## 132 Cpne7 7.274987e-09 0.5685972 0.128 0.018 1.784191e-04 Nb5d.INF
## 133 Phip 1.182723e-08 0.6130820 0.549 0.398 2.900628e-04 Nb5d.INF
## 134 Gm43391 1.505672e-08 0.6543351 0.236 0.095 3.692660e-04 Nb5d.INF
## 135 Pcsk2os21 1.539838e-08 0.7221605 0.479 0.311 3.776452e-04 Nb5d.INF
## 136 Gm10791 1.592993e-08 0.6780872 0.271 0.100 3.906815e-04 Nb5d.INF
## 137 Adk1 1.725555e-08 0.7317618 0.333 0.187 4.231924e-04 Nb5d.INF
## 138 Grid1 1.935615e-08 0.6716949 0.552 0.388 4.747096e-04 Nb5d.INF
## 139 Gm48283 2.086099e-08 0.5560521 0.253 0.084 5.116158e-04 Nb5d.INF
## 140 Tmem8b1 3.154339e-08 0.6416565 0.531 0.343 7.736017e-04 Nb5d.INF
## 141 Golga4 3.658875e-08 0.6061004 0.444 0.248 8.973392e-04 Nb5d.INF
## 142 Edil3 6.552941e-08 0.5935189 0.750 0.652 1.607109e-03 Nb5d.INF
## 143 Fry 7.536168e-08 0.5946945 0.681 0.533 1.848245e-03 Nb5d.INF
## 144 Dlg22 8.927252e-08 0.3143282 0.990 0.982 2.189409e-03 Nb5d.INF
## 145 Ccser12 9.331096e-08 0.4890291 0.795 0.755 2.288451e-03 Nb5d.INF
## 146 Unc13b1 1.047124e-07 0.5866863 0.500 0.322 2.568072e-03 Nb5d.INF
## 147 Tctex1d1 1.370861e-07 0.4333606 0.118 0.021 3.362036e-03 Nb5d.INF
## 148 Gm32647 1.646828e-07 0.8128257 0.153 0.037 4.038846e-03 Nb5d.INF
## 149 Plppr5 1.767306e-07 0.5268661 0.646 0.538 4.334318e-03 Nb5d.INF
## 150 Gm217981 2.912692e-07 0.7666057 0.236 0.103 7.143376e-03 Nb5d.INF
## 151 Rgs72 3.159307e-07 0.5758945 0.507 0.346 7.748200e-03 Nb5d.INF
## 152 Lingo1 4.172889e-07 0.5468335 0.201 0.063 1.023401e-02 Nb5d.INF
## 153 Mpc12 4.291280e-07 0.4957475 0.476 0.372 1.052436e-02 Nb5d.INF
## 154 Dennd4a 4.327968e-07 0.6384667 0.382 0.222 1.061434e-02 Nb5d.INF
## 155 Syt142 4.349981e-07 0.4951693 0.611 0.493 1.066833e-02 Nb5d.INF
## 156 Dock71 4.458412e-07 0.5863511 0.490 0.338 1.093426e-02 Nb5d.INF
## 157 Mapk8 4.473014e-07 0.6126404 0.444 0.266 1.097007e-02 Nb5d.INF
## 158 Setbp1 4.595514e-07 0.6281453 0.215 0.077 1.127050e-02 Nb5d.INF
## 159 Plcl11 4.939162e-07 0.4664003 0.896 0.855 1.211329e-02 Nb5d.INF
## 160 Npr21 5.335064e-07 0.5091585 0.587 0.459 1.308424e-02 Nb5d.INF
## 161 Map7d11 5.786101e-07 0.6218713 0.562 0.401 1.419041e-02 Nb5d.INF
## 162 Vwc2l1 1.223733e-06 0.5511667 0.128 0.029 3.001206e-02 Nb5d.INF
## 163 Rai21 1.249040e-06 0.5761029 0.240 0.106 3.063269e-02 Nb5d.INF
## 164 Npy1r1 1.635777e-06 0.4674979 0.128 0.026 4.011743e-02 Nb5d.INF
## 165 Lhfp 1.747721e-06 0.5367612 0.188 0.090 4.286286e-02 Nb5d.INF
## 166 Golm1 1.980890e-06 0.4664170 0.260 0.108 4.858133e-02 Nb5d.INF
## gene intAnno2
## 1 Zfp804a IPAN1
## 2 Fgf13 IPAN1
## 3 Gm15261 IPAN1
## 4 Cdh6 IPAN1
## 5 Tafa1 IPAN1
## 6 Luzp2 IPAN1
## 7 Ppp3ca IPAN1
## 8 Gm15680 IPAN1
## 9 Efr3a IPAN1
## 10 Lsamp IPAN1
## 11 Arhgap6 IPAN1
## 12 Ano2 IPAN1
## 13 Filip1 IPAN1
## 14 Otof IPAN1
## 15 Rab3c IPAN1
## 16 Rbfox1 IPAN1
## 17 Adgrg6 IPAN1
## 18 Ndufs1 IPAN1
## 19 Gpr85 IPAN1
## 20 Pdzrn4 IPAN1
## 21 Kctd12 IPAN1
## 22 Prom1 IPAN1
## 23 Gpr149 IPAN1
## 24 1810034E14Rik IPAN1
## 25 Cnr1 IPAN1
## 26 Cntn5 IPAN1
## 27 Gm16541 IPAN1
## 28 D030068K23Rik IPAN1
## 29 Fgfr2 IPAN1
## 30 Gm28750 IPAN1
## 31 Rnf152 IPAN1
## 32 Kcnb2 IPAN1
## 33 Nme6 IPAN1
## 34 1700010I02Rik IPAN1
## 35 Gm47153 IPAN1
## 36 Gm32335 IPAN1
## 37 Mef2c IPAN1
## 38 Dapk2 IPAN1
## 39 1110019D14Rik IPAN1
## 40 A330008L17Rik IPAN1
## 41 P2rx2 IPAN1
## 42 Hs3st5 IPAN1
## 43 Parm1 IPAN1
## 44 Nmu IPAN1
## 45 Cadm2 IPAN1
## 46 Sgip1 IPAN1
## 47 App IPAN1
## 48 Fam155a IPAN1
## 49 Dgki IPAN1
## 50 4930432L08Rik IPAN1
## 51 Kcnh8 IPAN1
## 52 Gng2 IPAN1
## 53 Cacnb4 IPAN1
## 54 Hnrnpll IPAN1
## 55 Col24a1 IPAN1
## 56 Gm26871 IPAN1
## 57 Alk IPAN1
## 58 Calcb IPAN1
## 59 Asxl3 IPAN1
## 60 Tmeff2 IPAN1
## 61 Nell1 IPAN1
## 62 Pcsk2 IPAN1
## 63 Abi3bp IPAN1
## 64 Igf1r IPAN1
## 65 Lingo2 IPAN1
## 66 Dpyd IPAN1
## 67 Tll1 IPAN1
## 68 Negr1 IPAN1
## 69 Dysf IPAN1
## 70 Syt9 IPAN1
## 71 Gm30094 IPAN1
## 72 Scn3a IPAN1
## 73 Epha7 IPAN1
## 74 Itih5 IPAN1
## 75 Syt1 IPAN1
## 76 Dgkg IPAN1
## 77 Ppm1h IPAN1
## 78 Ctnnd2 IPAN1
## 79 Galnt13 IPAN1
## 80 Gm12709 IPAN1
## 81 1700111E14Rik IPAN1
## 82 Epha6 IPAN1
## 83 Rcan3 IPAN1
## 84 Ptchd4 IPAN1
## 85 Rab27b IPAN1
## 86 Gm38405 IPAN1
## 87 Ppp1r12b IPAN1
## 88 Kif5a IPAN1
## 89 Pak7 IPAN1
## 90 Dner IPAN1
## 91 Trhde IPAN1
## 92 Tmcc3 IPAN1
## 93 Fhl1 IPAN1
## 94 Dclk1 IPAN1
## 95 Gria4 IPAN1
## 96 Grp IPAN1
## 97 Gpc6 IPAN1
## 98 Adcy2 IPAN1
## 99 Galm IPAN1
## 100 Aff2 IPAN1
## 101 Map3k3 IPAN1
## 102 Gm21847 IPAN1
## 103 Rock1 IPAN1
## 104 Apbb2 IPAN1
## 105 Adam12 IPAN1
## 106 Kcnt2 IPAN1
## 107 Kcna4 IPAN1
## 108 Myo3b IPAN1
## 109 Meg3 IPAN1
## 110 Gm16158 IPAN1
## 111 Bmpr2 IPAN1
## 112 Mgat5 IPAN1
## 113 Slco3a1 IPAN1
## 114 Raph1 IPAN1
## 115 Stxbp5l IPAN1
## 116 Large1 IPAN1
## 117 Trpm7 IPAN1
## 118 Antxr2 IPAN1
## 119 Gm26917 IPAN1
## 120 Gm49226 IPAN1
## 121 Atp6v0a1 IPAN1
## 122 Rad51b IPAN1
## 123 Gucy1a2 IPAN1
## 124 Grm7 IPAN1
## 125 Iqgap2 IPAN1
## 126 9530059O14Rik IPAN1
## 127 Fam117a IPAN1
## 128 Arpp21 IPAN1
## 129 Klf6 IPAN1
## 130 Ubr5 IPAN1
## 131 Nell1os IPAN1
## 132 Cpne7 IPAN1
## 133 Phip IPAN1
## 134 Gm43391 IPAN1
## 135 Pcsk2os2 IPAN1
## 136 Gm10791 IPAN1
## 137 Adk IPAN1
## 138 Grid1 IPAN1
## 139 Gm48283 IPAN1
## 140 Tmem8b IPAN1
## 141 Golga4 IPAN1
## 142 Edil3 IPAN1
## 143 Fry IPAN1
## 144 Dlg2 IPAN1
## 145 Ccser1 IPAN1
## 146 Unc13b IPAN1
## 147 Tctex1d1 IPAN1
## 148 Gm32647 IPAN1
## 149 Plppr5 IPAN1
## 150 Gm21798 IPAN1
## 151 Rgs7 IPAN1
## 152 Lingo1 IPAN1
## 153 Mpc1 IPAN1
## 154 Dennd4a IPAN1
## 155 Syt14 IPAN1
## 156 Dock7 IPAN1
## 157 Mapk8 IPAN1
## 158 Setbp1 IPAN1
## 159 Plcl1 IPAN1
## 160 Npr2 IPAN1
## 161 Map7d1 IPAN1
## 162 Vwc2l IPAN1
## 163 Rai2 IPAN1
## 164 Npy1r IPAN1
## 165 Lhfp IPAN1
## 166 Golm1 IPAN1df_test1.DEGs_new %>% filter(p_val_adj < cut.padj &
abs(avg_log2FC) > cut.log2FC &
pct.1 > cut.pct1) %>%
group_by(intAnno2,cluster) %>% filter ( intAnno2 == "IPAN2") %>% as.data.frame() ## X p_val avg_log2FC pct.1 pct.2 p_val_adj cluster
## 1 Dgkb 8.984111e-21 0.8476953 0.981 0.939 2.203353e-16 Nb5d.PBS
## 2 4930428E07Rik1 2.238643e-15 1.1678282 0.723 0.490 5.490272e-11 Nb5d.PBS
## 3 Ctnna33 3.705546e-15 1.1143216 0.714 0.453 9.087853e-11 Nb5d.PBS
## 4 Malat14 1.550451e-13 0.3418027 1.000 1.000 3.802481e-09 Nb5d.PBS
## 5 Sema5a1 7.882569e-10 0.6222987 0.818 0.759 1.933200e-05 Nb5d.PBS
## 6 Gm152612 2.218453e-09 0.8561602 0.770 0.584 5.440756e-05 Nb5d.PBS
## 7 1700024B18Rik4 7.271022e-09 1.0441764 0.522 0.318 1.783218e-04 Nb5d.PBS
## 8 Cdh91 1.170528e-08 0.7985568 0.613 0.535 2.870721e-04 Nb5d.PBS
## 9 Gm162261 8.795125e-08 0.6955327 0.758 0.604 2.157004e-03 Nb5d.PBS
## 10 Gm11339 2.127904e-07 0.7890379 0.519 0.310 5.218685e-03 Nb5d.PBS
## 11 Serpini11 3.762577e-07 0.5357463 0.877 0.747 9.227720e-03 Nb5d.PBS
## 12 C797981 3.804951e-07 0.5481464 0.742 0.563 9.331642e-03 Nb5d.PBS
## 13 Vcan 5.321809e-07 0.6817009 0.579 0.429 1.305174e-02 Nb5d.PBS
## 14 Cdh62 5.383586e-07 0.8382672 0.569 0.437 1.320324e-02 Nb5d.PBS
## 15 Gm268733 1.106992e-06 0.6864560 0.198 0.053 2.714898e-02 Nb5d.PBS
## 16 Fam155a4 9.169076e-58 0.8699845 1.000 1.000 2.248716e-53 Nb5d.INF
## 17 Large13 7.059124e-57 1.9174776 0.914 0.591 1.731250e-52 Nb5d.INF
## 18 Gm306132 1.567105e-50 3.1018890 0.522 0.016 3.843326e-46 Nb5d.INF
## 19 Nrxn32 5.808554e-39 1.0873957 1.000 0.981 1.424548e-34 Nb5d.INF
## 20 Pcsk23 8.201326e-35 1.5194388 0.824 0.550 2.011375e-30 Nb5d.INF
## 21 Gm218472 3.677600e-34 2.0618649 0.437 0.031 9.019315e-30 Nb5d.INF
## 22 Ptprz11 1.245238e-24 1.4224892 0.384 0.047 3.053946e-20 Nb5d.INF
## 23 Kcnd22 1.267332e-23 1.1554087 0.882 0.698 3.108133e-19 Nb5d.INF
## 24 Alk3 2.283223e-23 0.6509571 1.000 0.981 5.599604e-19 Nb5d.INF
## 25 Calcb2 3.804532e-23 1.5970568 0.441 0.088 9.330614e-19 Nb5d.INF
## 26 Cacnb42 6.012505e-23 1.3984606 0.673 0.343 1.474567e-18 Nb5d.INF
## 27 Rab27b2 3.105717e-22 1.4279048 0.637 0.324 7.616770e-18 Nb5d.INF
## 28 Ppm1h3 1.240212e-20 0.9632908 0.869 0.648 3.041620e-16 Nb5d.INF
## 29 Tmcc33 2.770626e-19 1.3141965 0.588 0.236 6.794961e-15 Nb5d.INF
## 30 Gm217982 3.832938e-19 1.3822856 0.343 0.053 9.400280e-15 Nb5d.INF
## 31 Trpc3 5.466642e-19 1.2554502 0.400 0.088 1.340694e-14 Nb5d.INF
## 32 Arid5b1 8.050754e-18 1.0628226 0.727 0.459 1.974447e-13 Nb5d.INF
## 33 Nell13 2.710122e-16 1.2343783 0.637 0.384 6.646574e-12 Nb5d.INF
## 34 Dclk13 2.649230e-15 0.8906174 0.792 0.594 6.497236e-11 Nb5d.INF
## 35 Oxr13 9.306898e-15 0.9128341 0.878 0.673 2.282517e-10 Nb5d.INF
## 36 Sema3c 5.121192e-14 0.8772198 0.257 0.038 1.255972e-09 Nb5d.INF
## 37 Galnt132 5.502152e-14 0.5504102 0.992 0.969 1.349403e-09 Nb5d.INF
## 38 Gm300942 7.512355e-14 1.2692612 0.241 0.035 1.842405e-09 Nb5d.INF
## 39 Adcy82 8.634644e-14 0.9348883 0.241 0.035 2.117647e-09 Nb5d.INF
## 40 Dlg23 2.599861e-13 0.3869284 0.996 1.000 6.376159e-09 Nb5d.INF
## 41 Kcnq51 5.771307e-13 1.0223286 0.633 0.340 1.415413e-08 Nb5d.INF
## 42 Pcsk2os22 7.571718e-13 1.1267092 0.429 0.157 1.856964e-08 Nb5d.INF
## 43 Rgs73 2.245349e-11 0.7369429 0.751 0.538 5.506718e-07 Nb5d.INF
## 44 Asxl32 1.057437e-10 0.9250655 0.469 0.226 2.593365e-06 Nb5d.INF
## 45 Nkain22 2.101811e-10 0.7923540 0.780 0.569 5.154691e-06 Nb5d.INF
## 46 Stxbp5l2 2.810586e-10 0.4609672 0.963 0.921 6.892961e-06 Nb5d.INF
## 47 Csmd1 3.457238e-10 1.0416354 0.531 0.318 8.478876e-06 Nb5d.INF
## 48 Grm71 3.459586e-10 0.5185850 0.971 0.943 8.484635e-06 Nb5d.INF
## 49 Necab11 4.137618e-10 0.9119567 0.461 0.220 1.014751e-05 Nb5d.INF
## 50 App2 7.087319e-10 0.5494284 0.873 0.723 1.738165e-05 Nb5d.INF
## 51 Ppp1r12b2 7.961459e-10 0.7238245 0.653 0.475 1.952548e-05 Nb5d.INF
## 52 Dner3 1.561561e-09 0.6630562 0.812 0.619 3.829727e-05 Nb5d.INF
## 53 Cyyr13 2.378190e-09 0.7877871 0.310 0.101 5.832512e-05 Nb5d.INF
## 54 Scn3a2 3.010546e-09 0.7189504 0.731 0.544 7.383365e-05 Nb5d.INF
## 55 Fras1 6.568307e-09 0.7411780 0.196 0.038 1.610877e-04 Nb5d.INF
## 56 Dlc12 6.919855e-09 0.6193986 0.869 0.777 1.697094e-04 Nb5d.INF
## 57 Pcdh11x1 6.988003e-09 1.1498161 0.380 0.186 1.713808e-04 Nb5d.INF
## 58 Hnrnpll2 8.302118e-09 0.8569050 0.286 0.091 2.036094e-04 Nb5d.INF
## 59 Ptchd41 1.250139e-08 0.8004919 0.249 0.069 3.065965e-04 Nb5d.INF
## 60 Dmd1 1.653224e-08 0.6144068 0.890 0.780 4.054531e-04 Nb5d.INF
## 61 Antxr22 6.014578e-08 0.8743540 0.433 0.239 1.475075e-03 Nb5d.INF
## 62 Gabrg32 9.216211e-08 0.3587327 0.996 0.987 2.260276e-03 Nb5d.INF
## 63 Tll12 1.475759e-07 0.7194884 0.159 0.028 3.619298e-03 Nb5d.INF
## 64 Gucy1b11 2.667132e-07 0.7414708 0.371 0.170 6.541140e-03 Nb5d.INF
## 65 Hlf1 4.236502e-07 0.7802997 0.420 0.223 1.039002e-02 Nb5d.INF
## 66 Lrch12 4.276195e-07 0.6891340 0.306 0.123 1.048737e-02 Nb5d.INF
## 67 Col24a12 5.778937e-07 0.9348384 0.196 0.057 1.417284e-02 Nb5d.INF
## 68 Thsd7b3 6.464770e-07 0.7405743 0.620 0.453 1.585485e-02 Nb5d.INF
## 69 Cdh122 6.873839e-07 0.7159602 0.196 0.053 1.685809e-02 Nb5d.INF
## 70 Adk2 8.215702e-07 0.8327246 0.331 0.167 2.014901e-02 Nb5d.INF
## 71 Pkp42 1.050410e-06 0.6597114 0.535 0.368 2.576130e-02 Nb5d.INF
## 72 Unc13b2 1.072077e-06 0.6349907 0.506 0.327 2.629270e-02 Nb5d.INF
## 73 Col11a1 1.085435e-06 0.6554471 0.114 0.016 2.662028e-02 Nb5d.INF
## 74 Gria41 1.400895e-06 0.5285050 0.841 0.704 3.435695e-02 Nb5d.INF
## 75 Grid2 1.655054e-06 0.4222820 0.992 0.950 4.059020e-02 Nb5d.INF
## 76 Zdhhc143 1.716447e-06 0.5650068 0.473 0.264 4.209586e-02 Nb5d.INF
## 77 Prune23 1.831994e-06 0.4455636 0.951 0.884 4.492966e-02 Nb5d.INF
## 78 Ncam2 2.014743e-06 0.3925894 0.955 0.940 4.941158e-02 Nb5d.INF
## gene intAnno2
## 1 Dgkb IPAN2
## 2 4930428E07Rik IPAN2
## 3 Ctnna3 IPAN2
## 4 Malat1 IPAN2
## 5 Sema5a IPAN2
## 6 Gm15261 IPAN2
## 7 1700024B18Rik IPAN2
## 8 Cdh9 IPAN2
## 9 Gm16226 IPAN2
## 10 Gm11339 IPAN2
## 11 Serpini1 IPAN2
## 12 C79798 IPAN2
## 13 Vcan IPAN2
## 14 Cdh6 IPAN2
## 15 Gm26873 IPAN2
## 16 Fam155a IPAN2
## 17 Large1 IPAN2
## 18 Gm30613 IPAN2
## 19 Nrxn3 IPAN2
## 20 Pcsk2 IPAN2
## 21 Gm21847 IPAN2
## 22 Ptprz1 IPAN2
## 23 Kcnd2 IPAN2
## 24 Alk IPAN2
## 25 Calcb IPAN2
## 26 Cacnb4 IPAN2
## 27 Rab27b IPAN2
## 28 Ppm1h IPAN2
## 29 Tmcc3 IPAN2
## 30 Gm21798 IPAN2
## 31 Trpc3 IPAN2
## 32 Arid5b IPAN2
## 33 Nell1 IPAN2
## 34 Dclk1 IPAN2
## 35 Oxr1 IPAN2
## 36 Sema3c IPAN2
## 37 Galnt13 IPAN2
## 38 Gm30094 IPAN2
## 39 Adcy8 IPAN2
## 40 Dlg2 IPAN2
## 41 Kcnq5 IPAN2
## 42 Pcsk2os2 IPAN2
## 43 Rgs7 IPAN2
## 44 Asxl3 IPAN2
## 45 Nkain2 IPAN2
## 46 Stxbp5l IPAN2
## 47 Csmd1 IPAN2
## 48 Grm7 IPAN2
## 49 Necab1 IPAN2
## 50 App IPAN2
## 51 Ppp1r12b IPAN2
## 52 Dner IPAN2
## 53 Cyyr1 IPAN2
## 54 Scn3a IPAN2
## 55 Fras1 IPAN2
## 56 Dlc1 IPAN2
## 57 Pcdh11x IPAN2
## 58 Hnrnpll IPAN2
## 59 Ptchd4 IPAN2
## 60 Dmd IPAN2
## 61 Antxr2 IPAN2
## 62 Gabrg3 IPAN2
## 63 Tll1 IPAN2
## 64 Gucy1b1 IPAN2
## 65 Hlf IPAN2
## 66 Lrch1 IPAN2
## 67 Col24a1 IPAN2
## 68 Thsd7b IPAN2
## 69 Cdh12 IPAN2
## 70 Adk IPAN2
## 71 Pkp4 IPAN2
## 72 Unc13b IPAN2
## 73 Col11a1 IPAN2
## 74 Gria4 IPAN2
## 75 Grid2 IPAN2
## 76 Zdhhc14 IPAN2
## 77 Prune2 IPAN2
## 78 Ncam2 IPAN2pp.stat.DEG <- list()pp.stat.DEG[[1]] <- df_test1.DEGs_new %>% filter(p_val_adj < cut.padj &
abs(avg_log2FC) > cut.log2FC &
pct.1 > cut.pct1) %>%
group_by(intAnno2,cluster) %>%
summarise(up.DEGs = n()) %>% as.data.frame() %>%
ggplot(aes(x=intAnno2, y=up.DEGs, color = cluster)) +
geom_bar(stat="summary", fun="mean", position = position_dodge(0.75), width = 0.58, fill="white") +
theme_classic(base_size = 15) +
scale_color_manual(values = color.test1, name="") +
labs(title=paste0("up.DEGs stat, pct.1>",cut.pct1,", padj<",cut.padj,", |log2FC|>",cut.log2FC), y = "Proportion") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6),
title =element_text(size=12, face='bold'))## `summarise()` has grouped output by 'intAnno2'. You can override using the
## `.groups` argument.pp.stat.DEG[[1]]
DEGs.IPAN1 <- (df_test1.DEGs_new %>% filter(p_val_adj < cut.padj &
abs(avg_log2FC) > cut.log2FC &
pct.1 > cut.pct1) %>%
group_by(intAnno2,cluster) %>% filter ( intAnno2 == "IPAN1") %>% as.data.frame())$gene
DEGs.IPAN2 <- (df_test1.DEGs_new %>% filter(p_val_adj < cut.padj &
abs(avg_log2FC) > cut.log2FC &
pct.1 > cut.pct1) %>%
group_by(intAnno2,cluster) %>% filter ( intAnno2 == "IPAN2") %>% as.data.frame())$genepp.cut1.IPAN1 <- DoHeatmap(subset(test1.seur, subset = intAnno1 == "IPAN1"), slot = "scale.data", disp.min = -1, disp.max = 2,
features = DEGs.IPAN1[c(43:166,1:42)], group.by = "cnt1",
group.colors = color.test1) + guides(color=FALSE) + theme(axis.text.y = element_text(size=2.25),
plot.margin = unit(c(0.3,0.3,0.3,0.3),"cm")) +
labs(title = "cut1, IPAN1")
pp.cut1.IPAN1
pp.cut1.IPAN2 <- DoHeatmap(subset(test1.seur, subset = intAnno1 == "IPAN2"), slot = "scale.data", disp.min = -1, disp.max = 2,
features = DEGs.IPAN2[c(16:78,1:15)], group.by = "cnt1",
group.colors = color.test1) + guides(color=FALSE) + theme(axis.text.y = element_text(size=2.4),
plot.margin = unit(c(0.3,0.3,0.3,0.3),"cm")) +
labs(title = "cut1, IPAN2")
pp.cut1.IPAN2
## cut2
cut.padj = 0.01
cut.log2FC = log2(1.5)
cut.pct1 = 0.1
df_test1.DEGs_new %>% filter(p_val_adj < cut.padj &
abs(avg_log2FC) > cut.log2FC &
pct.1 > cut.pct1) %>%
group_by(intAnno2,cluster) %>% filter ( intAnno2 == "IPAN1") %>% as.data.frame()## X p_val avg_log2FC pct.1 pct.2 p_val_adj cluster
## 1 Zfp804a1 2.807963e-43 0.9178380 0.997 0.983 6.886529e-39 Nb5d.PBS
## 2 Fgf131 2.599880e-39 0.8256163 1.000 0.993 6.376205e-35 Nb5d.PBS
## 3 Gm152611 1.174558e-27 1.2851620 0.865 0.583 2.880603e-23 Nb5d.PBS
## 4 Cdh61 2.345303e-27 0.8959969 0.926 0.806 5.751856e-23 Nb5d.PBS
## 5 Luzp21 8.214889e-20 1.2231915 0.623 0.288 2.014702e-15 Nb5d.PBS
## 6 Gm156801 3.777871e-16 1.1592365 0.480 0.194 9.265228e-12 Nb5d.PBS
## 7 Efr3a1 4.417514e-16 0.6065075 0.929 0.830 1.083395e-11 Nb5d.PBS
## 8 Arhgap61 4.007052e-13 0.9236485 0.628 0.392 9.827294e-09 Nb5d.PBS
## 9 Otof 1.540171e-12 0.9306861 0.393 0.142 3.777270e-08 Nb5d.PBS
## 10 Adgrg61 3.451285e-10 0.6935988 0.834 0.753 8.464277e-06 Nb5d.PBS
## 11 Ndufs12 5.810237e-10 0.8387902 0.404 0.188 1.424961e-05 Nb5d.PBS
## 12 Gpr851 5.990973e-10 0.7349275 0.488 0.260 1.469286e-05 Nb5d.PBS
## 13 Pdzrn41 1.691186e-09 0.6758964 0.757 0.625 4.147634e-05 Nb5d.PBS
## 14 Kctd121 3.142396e-09 0.6097306 0.256 0.076 7.706726e-05 Nb5d.PBS
## 15 Prom11 1.004221e-08 0.7204048 0.330 0.135 2.462852e-04 Nb5d.PBS
## 16 1810034E14Rik1 1.878015e-08 0.6179254 0.612 0.385 4.605831e-04 Nb5d.PBS
## 17 Gm16541 6.603472e-08 0.7159641 0.575 0.396 1.619501e-03 Nb5d.PBS
## 18 D030068K23Rik1 9.186788e-08 0.6251514 0.485 0.271 2.253060e-03 Nb5d.PBS
## 19 Gm287502 1.094314e-07 0.6274474 0.435 0.229 2.683804e-03 Nb5d.PBS
## 20 Parm12 1.075991e-104 2.1724505 0.931 0.493 2.638869e-100 Nb5d.INF
## 21 Nmu1 2.136217e-82 2.0692761 0.962 0.546 5.239073e-78 Nb5d.INF
## 22 Cadm22 2.247800e-73 2.6501830 0.819 0.285 5.512730e-69 Nb5d.INF
## 23 Sgip11 7.278459e-69 1.3828413 0.965 0.789 1.785042e-64 Nb5d.INF
## 24 App1 1.928396e-66 1.4019565 0.965 0.768 4.729391e-62 Nb5d.INF
## 25 Fam155a3 5.924580e-64 1.1345751 1.000 0.989 1.453003e-59 Nb5d.INF
## 26 Dgki1 8.188198e-60 0.9389057 1.000 0.997 2.008156e-55 Nb5d.INF
## 27 4930432L08Rik1 4.960967e-58 2.0677918 0.847 0.401 1.216677e-53 Nb5d.INF
## 28 Kcnh82 9.483323e-51 1.9055502 0.642 0.137 2.325785e-46 Nb5d.INF
## 29 Gng21 2.747814e-48 1.4543326 0.833 0.533 6.739014e-44 Nb5d.INF
## 30 Cacnb41 1.137876e-43 1.6083705 0.691 0.266 2.790640e-39 Nb5d.INF
## 31 Hnrnpll1 1.164547e-42 1.5964868 0.615 0.179 2.856051e-38 Nb5d.INF
## 32 Col24a11 2.666312e-40 2.6309109 0.385 0.016 6.539131e-36 Nb5d.INF
## 33 Gm268712 3.622215e-40 0.8745574 0.986 0.950 8.883482e-36 Nb5d.INF
## 34 Alk2 2.235025e-34 1.8410770 0.635 0.277 5.481399e-30 Nb5d.INF
## 35 Calcb1 3.010440e-34 1.2126176 0.833 0.615 7.383104e-30 Nb5d.INF
## 36 Asxl31 3.433278e-34 1.3130268 0.639 0.216 8.420115e-30 Nb5d.INF
## 37 Tmeff21 2.592977e-33 1.0129207 0.955 0.934 6.359276e-29 Nb5d.INF
## 38 Nell12 5.890455e-32 0.9233766 0.927 0.807 1.444634e-27 Nb5d.INF
## 39 Pcsk22 1.580598e-31 1.0221099 0.882 0.786 3.876417e-27 Nb5d.INF
## 40 Abi3bp1 6.445359e-29 1.5634638 0.410 0.063 1.580724e-24 Nb5d.INF
## 41 Igf1r1 1.716889e-28 0.9882020 0.917 0.815 4.210671e-24 Nb5d.INF
## 42 Lingo22 2.992058e-28 0.8543086 0.997 0.979 7.338023e-24 Nb5d.INF
## 43 Dpyd 1.594534e-27 0.8040543 0.979 0.887 3.910595e-23 Nb5d.INF
## 44 Tll11 5.407854e-26 1.3664326 0.361 0.047 1.326276e-21 Nb5d.INF
## 45 Negr1 1.503459e-25 0.9415294 0.882 0.728 3.687234e-21 Nb5d.INF
## 46 Dysf1 6.038896e-24 1.3775355 0.583 0.288 1.481039e-19 Nb5d.INF
## 47 Syt91 1.700982e-23 0.9292975 0.826 0.641 4.171659e-19 Nb5d.INF
## 48 Gm300941 2.054852e-23 1.3294833 0.351 0.055 5.039524e-19 Nb5d.INF
## 49 Scn3a1 2.128004e-23 0.8035418 0.906 0.770 5.218930e-19 Nb5d.INF
## 50 Epha71 1.243026e-22 1.3068367 0.469 0.140 3.048520e-18 Nb5d.INF
## 51 Itih5 2.987203e-22 0.9396656 0.240 0.011 7.326115e-18 Nb5d.INF
## 52 Ppm1h2 6.613761e-21 0.8237434 0.882 0.707 1.622025e-16 Nb5d.INF
## 53 Ctnnd2 1.919416e-19 0.8719439 0.788 0.541 4.707367e-15 Nb5d.INF
## 54 Galnt131 2.130175e-19 1.0822264 0.708 0.501 5.224254e-15 Nb5d.INF
## 55 Gm12709 3.326302e-19 1.0902550 0.601 0.361 8.157756e-15 Nb5d.INF
## 56 1700111E14Rik1 4.880224e-19 0.7344907 0.934 0.950 1.196875e-14 Nb5d.INF
## 57 Epha6 1.668328e-18 0.9320389 0.795 0.649 4.091574e-14 Nb5d.INF
## 58 Rcan31 3.345314e-18 0.9868467 0.431 0.150 8.204383e-14 Nb5d.INF
## 59 Ptchd4 1.549455e-17 0.7943816 0.188 0.008 3.800038e-13 Nb5d.INF
## 60 Rab27b1 1.699653e-17 1.0190557 0.628 0.467 4.168399e-13 Nb5d.INF
## 61 Gm38405 1.702483e-17 1.4303310 0.191 0.011 4.175340e-13 Nb5d.INF
## 62 Ppp1r12b1 4.474937e-17 0.7289944 0.851 0.683 1.097478e-12 Nb5d.INF
## 63 Kif5a1 1.926266e-16 0.7521534 0.837 0.665 4.724166e-12 Nb5d.INF
## 64 Pak71 3.402264e-16 0.7740531 0.792 0.609 8.344053e-12 Nb5d.INF
## 65 Dner2 4.474026e-16 0.9619404 0.441 0.169 1.097255e-11 Nb5d.INF
## 66 Trhde 5.593460e-16 0.6860635 0.149 0.003 1.371796e-11 Nb5d.INF
## 67 Tmcc32 1.420198e-15 1.0647123 0.444 0.172 3.483036e-11 Nb5d.INF
## 68 Fhl11 1.595311e-15 0.9481742 0.559 0.317 3.912501e-11 Nb5d.INF
## 69 Dclk12 3.483586e-15 0.7298482 0.844 0.710 8.543495e-11 Nb5d.INF
## 70 Gria4 5.262180e-15 0.8870638 0.688 0.470 1.290550e-10 Nb5d.INF
## 71 Grp1 1.017341e-14 0.8228428 0.267 0.050 2.495029e-10 Nb5d.INF
## 72 Gpc6 2.869327e-14 1.4548572 0.615 0.383 7.037024e-10 Nb5d.INF
## 73 Adcy2 3.184482e-14 0.7526644 0.205 0.024 7.809942e-10 Nb5d.INF
## 74 Galm 6.957875e-14 0.7771259 0.205 0.029 1.706419e-09 Nb5d.INF
## 75 Aff23 7.761133e-14 0.5975160 0.875 0.699 1.903418e-09 Nb5d.INF
## 76 Map3k31 8.886742e-14 0.8545849 0.552 0.332 2.179473e-09 Nb5d.INF
## 77 Rock11 4.300319e-12 0.7451907 0.674 0.475 1.054653e-07 Nb5d.INF
## 78 Apbb21 6.333930e-12 0.7920275 0.524 0.309 1.553396e-07 Nb5d.INF
## 79 Adam121 6.450169e-12 0.8857523 0.420 0.201 1.581904e-07 Nb5d.INF
## 80 Kcnt21 6.869812e-12 0.6137030 0.934 0.894 1.684821e-07 Nb5d.INF
## 81 Kcna4 6.995028e-12 0.7228218 0.215 0.045 1.715531e-07 Nb5d.INF
## 82 Myo3b 8.083859e-12 1.0179703 0.167 0.018 1.982566e-07 Nb5d.INF
## 83 Gm16158 2.216743e-11 0.7599130 0.201 0.040 5.436563e-07 Nb5d.INF
## 84 Bmpr21 2.647548e-11 0.6808265 0.632 0.456 6.493112e-07 Nb5d.INF
## 85 Mgat52 4.044154e-11 0.6689303 0.493 0.343 9.918288e-07 Nb5d.INF
## 86 Slco3a12 4.187152e-11 0.7235636 0.733 0.644 1.026899e-06 Nb5d.INF
## 87 Raph11 4.334610e-11 0.7335959 0.611 0.383 1.063063e-06 Nb5d.INF
## 88 Large12 5.533504e-11 0.7115061 0.760 0.654 1.357092e-06 Nb5d.INF
## 89 Trpm71 7.170477e-11 0.6973080 0.597 0.361 1.758560e-06 Nb5d.INF
## 90 Antxr21 8.919431e-11 0.8469972 0.392 0.195 2.187491e-06 Nb5d.INF
## 91 Gm269173 1.344304e-10 0.7888886 0.722 0.604 3.296906e-06 Nb5d.INF
## 92 Gm49226 2.864540e-10 0.7784810 0.378 0.169 7.025283e-06 Nb5d.INF
## 93 Rad51b 5.457911e-10 0.7331041 0.177 0.032 1.338553e-05 Nb5d.INF
## 94 Gucy1a22 6.271109e-10 0.6683144 0.701 0.522 1.537990e-05 Nb5d.INF
## 95 Grm7 1.107330e-09 0.6650001 0.740 0.668 2.715727e-05 Nb5d.INF
## 96 Iqgap21 1.277660e-09 0.5961049 0.667 0.536 3.133460e-05 Nb5d.INF
## 97 Fam117a 3.004610e-09 0.7980456 0.326 0.140 7.368806e-05 Nb5d.INF
## 98 Arpp211 3.073181e-09 0.8474036 0.358 0.172 7.536977e-05 Nb5d.INF
## 99 Klf6 4.095137e-09 0.6453736 0.403 0.193 1.004332e-04 Nb5d.INF
## 100 Nell1os1 6.082172e-09 0.7021925 0.608 0.475 1.491653e-04 Nb5d.INF
## 101 Phip 1.182723e-08 0.6130820 0.549 0.398 2.900628e-04 Nb5d.INF
## 102 Gm43391 1.505672e-08 0.6543351 0.236 0.095 3.692660e-04 Nb5d.INF
## 103 Pcsk2os21 1.539838e-08 0.7221605 0.479 0.311 3.776452e-04 Nb5d.INF
## 104 Gm10791 1.592993e-08 0.6780872 0.271 0.100 3.906815e-04 Nb5d.INF
## 105 Adk1 1.725555e-08 0.7317618 0.333 0.187 4.231924e-04 Nb5d.INF
## 106 Grid1 1.935615e-08 0.6716949 0.552 0.388 4.747096e-04 Nb5d.INF
## 107 Tmem8b1 3.154339e-08 0.6416565 0.531 0.343 7.736017e-04 Nb5d.INF
## 108 Golga4 3.658875e-08 0.6061004 0.444 0.248 8.973392e-04 Nb5d.INF
## 109 Edil3 6.552941e-08 0.5935189 0.750 0.652 1.607109e-03 Nb5d.INF
## 110 Fry 7.536168e-08 0.5946945 0.681 0.533 1.848245e-03 Nb5d.INF
## 111 Unc13b1 1.047124e-07 0.5866863 0.500 0.322 2.568072e-03 Nb5d.INF
## 112 Gm32647 1.646828e-07 0.8128257 0.153 0.037 4.038846e-03 Nb5d.INF
## 113 Gm217981 2.912692e-07 0.7666057 0.236 0.103 7.143376e-03 Nb5d.INF
## gene intAnno2
## 1 Zfp804a IPAN1
## 2 Fgf13 IPAN1
## 3 Gm15261 IPAN1
## 4 Cdh6 IPAN1
## 5 Luzp2 IPAN1
## 6 Gm15680 IPAN1
## 7 Efr3a IPAN1
## 8 Arhgap6 IPAN1
## 9 Otof IPAN1
## 10 Adgrg6 IPAN1
## 11 Ndufs1 IPAN1
## 12 Gpr85 IPAN1
## 13 Pdzrn4 IPAN1
## 14 Kctd12 IPAN1
## 15 Prom1 IPAN1
## 16 1810034E14Rik IPAN1
## 17 Gm16541 IPAN1
## 18 D030068K23Rik IPAN1
## 19 Gm28750 IPAN1
## 20 Parm1 IPAN1
## 21 Nmu IPAN1
## 22 Cadm2 IPAN1
## 23 Sgip1 IPAN1
## 24 App IPAN1
## 25 Fam155a IPAN1
## 26 Dgki IPAN1
## 27 4930432L08Rik IPAN1
## 28 Kcnh8 IPAN1
## 29 Gng2 IPAN1
## 30 Cacnb4 IPAN1
## 31 Hnrnpll IPAN1
## 32 Col24a1 IPAN1
## 33 Gm26871 IPAN1
## 34 Alk IPAN1
## 35 Calcb IPAN1
## 36 Asxl3 IPAN1
## 37 Tmeff2 IPAN1
## 38 Nell1 IPAN1
## 39 Pcsk2 IPAN1
## 40 Abi3bp IPAN1
## 41 Igf1r IPAN1
## 42 Lingo2 IPAN1
## 43 Dpyd IPAN1
## 44 Tll1 IPAN1
## 45 Negr1 IPAN1
## 46 Dysf IPAN1
## 47 Syt9 IPAN1
## 48 Gm30094 IPAN1
## 49 Scn3a IPAN1
## 50 Epha7 IPAN1
## 51 Itih5 IPAN1
## 52 Ppm1h IPAN1
## 53 Ctnnd2 IPAN1
## 54 Galnt13 IPAN1
## 55 Gm12709 IPAN1
## 56 1700111E14Rik IPAN1
## 57 Epha6 IPAN1
## 58 Rcan3 IPAN1
## 59 Ptchd4 IPAN1
## 60 Rab27b IPAN1
## 61 Gm38405 IPAN1
## 62 Ppp1r12b IPAN1
## 63 Kif5a IPAN1
## 64 Pak7 IPAN1
## 65 Dner IPAN1
## 66 Trhde IPAN1
## 67 Tmcc3 IPAN1
## 68 Fhl1 IPAN1
## 69 Dclk1 IPAN1
## 70 Gria4 IPAN1
## 71 Grp IPAN1
## 72 Gpc6 IPAN1
## 73 Adcy2 IPAN1
## 74 Galm IPAN1
## 75 Aff2 IPAN1
## 76 Map3k3 IPAN1
## 77 Rock1 IPAN1
## 78 Apbb2 IPAN1
## 79 Adam12 IPAN1
## 80 Kcnt2 IPAN1
## 81 Kcna4 IPAN1
## 82 Myo3b IPAN1
## 83 Gm16158 IPAN1
## 84 Bmpr2 IPAN1
## 85 Mgat5 IPAN1
## 86 Slco3a1 IPAN1
## 87 Raph1 IPAN1
## 88 Large1 IPAN1
## 89 Trpm7 IPAN1
## 90 Antxr2 IPAN1
## 91 Gm26917 IPAN1
## 92 Gm49226 IPAN1
## 93 Rad51b IPAN1
## 94 Gucy1a2 IPAN1
## 95 Grm7 IPAN1
## 96 Iqgap2 IPAN1
## 97 Fam117a IPAN1
## 98 Arpp21 IPAN1
## 99 Klf6 IPAN1
## 100 Nell1os IPAN1
## 101 Phip IPAN1
## 102 Gm43391 IPAN1
## 103 Pcsk2os2 IPAN1
## 104 Gm10791 IPAN1
## 105 Adk IPAN1
## 106 Grid1 IPAN1
## 107 Tmem8b IPAN1
## 108 Golga4 IPAN1
## 109 Edil3 IPAN1
## 110 Fry IPAN1
## 111 Unc13b IPAN1
## 112 Gm32647 IPAN1
## 113 Gm21798 IPAN1df_test1.DEGs_new %>% filter(p_val_adj < cut.padj &
abs(avg_log2FC) > cut.log2FC &
pct.1 > cut.pct1) %>%
group_by(intAnno2,cluster) %>% filter ( intAnno2 == "IPAN2") %>% as.data.frame() ## X p_val avg_log2FC pct.1 pct.2 p_val_adj cluster
## 1 Dgkb 8.984111e-21 0.8476953 0.981 0.939 2.203353e-16 Nb5d.PBS
## 2 4930428E07Rik1 2.238643e-15 1.1678282 0.723 0.490 5.490272e-11 Nb5d.PBS
## 3 Ctnna33 3.705546e-15 1.1143216 0.714 0.453 9.087853e-11 Nb5d.PBS
## 4 Sema5a1 7.882569e-10 0.6222987 0.818 0.759 1.933200e-05 Nb5d.PBS
## 5 Gm152612 2.218453e-09 0.8561602 0.770 0.584 5.440756e-05 Nb5d.PBS
## 6 1700024B18Rik4 7.271022e-09 1.0441764 0.522 0.318 1.783218e-04 Nb5d.PBS
## 7 Cdh91 1.170528e-08 0.7985568 0.613 0.535 2.870721e-04 Nb5d.PBS
## 8 Gm162261 8.795125e-08 0.6955327 0.758 0.604 2.157004e-03 Nb5d.PBS
## 9 Gm11339 2.127904e-07 0.7890379 0.519 0.310 5.218685e-03 Nb5d.PBS
## 10 Fam155a4 9.169076e-58 0.8699845 1.000 1.000 2.248716e-53 Nb5d.INF
## 11 Large13 7.059124e-57 1.9174776 0.914 0.591 1.731250e-52 Nb5d.INF
## 12 Gm306132 1.567105e-50 3.1018890 0.522 0.016 3.843326e-46 Nb5d.INF
## 13 Nrxn32 5.808554e-39 1.0873957 1.000 0.981 1.424548e-34 Nb5d.INF
## 14 Pcsk23 8.201326e-35 1.5194388 0.824 0.550 2.011375e-30 Nb5d.INF
## 15 Gm218472 3.677600e-34 2.0618649 0.437 0.031 9.019315e-30 Nb5d.INF
## 16 Ptprz11 1.245238e-24 1.4224892 0.384 0.047 3.053946e-20 Nb5d.INF
## 17 Kcnd22 1.267332e-23 1.1554087 0.882 0.698 3.108133e-19 Nb5d.INF
## 18 Alk3 2.283223e-23 0.6509571 1.000 0.981 5.599604e-19 Nb5d.INF
## 19 Calcb2 3.804532e-23 1.5970568 0.441 0.088 9.330614e-19 Nb5d.INF
## 20 Cacnb42 6.012505e-23 1.3984606 0.673 0.343 1.474567e-18 Nb5d.INF
## 21 Rab27b2 3.105717e-22 1.4279048 0.637 0.324 7.616770e-18 Nb5d.INF
## 22 Ppm1h3 1.240212e-20 0.9632908 0.869 0.648 3.041620e-16 Nb5d.INF
## 23 Tmcc33 2.770626e-19 1.3141965 0.588 0.236 6.794961e-15 Nb5d.INF
## 24 Gm217982 3.832938e-19 1.3822856 0.343 0.053 9.400280e-15 Nb5d.INF
## 25 Trpc3 5.466642e-19 1.2554502 0.400 0.088 1.340694e-14 Nb5d.INF
## 26 Arid5b1 8.050754e-18 1.0628226 0.727 0.459 1.974447e-13 Nb5d.INF
## 27 Nell13 2.710122e-16 1.2343783 0.637 0.384 6.646574e-12 Nb5d.INF
## 28 Dclk13 2.649230e-15 0.8906174 0.792 0.594 6.497236e-11 Nb5d.INF
## 29 Oxr13 9.306898e-15 0.9128341 0.878 0.673 2.282517e-10 Nb5d.INF
## 30 Sema3c 5.121192e-14 0.8772198 0.257 0.038 1.255972e-09 Nb5d.INF
## 31 Gm300942 7.512355e-14 1.2692612 0.241 0.035 1.842405e-09 Nb5d.INF
## 32 Adcy82 8.634644e-14 0.9348883 0.241 0.035 2.117647e-09 Nb5d.INF
## 33 Kcnq51 5.771307e-13 1.0223286 0.633 0.340 1.415413e-08 Nb5d.INF
## 34 Pcsk2os22 7.571718e-13 1.1267092 0.429 0.157 1.856964e-08 Nb5d.INF
## 35 Rgs73 2.245349e-11 0.7369429 0.751 0.538 5.506718e-07 Nb5d.INF
## 36 Asxl32 1.057437e-10 0.9250655 0.469 0.226 2.593365e-06 Nb5d.INF
## 37 Nkain22 2.101811e-10 0.7923540 0.780 0.569 5.154691e-06 Nb5d.INF
## 38 Csmd1 3.457238e-10 1.0416354 0.531 0.318 8.478876e-06 Nb5d.INF
## 39 Necab11 4.137618e-10 0.9119567 0.461 0.220 1.014751e-05 Nb5d.INF
## 40 Ppp1r12b2 7.961459e-10 0.7238245 0.653 0.475 1.952548e-05 Nb5d.INF
## 41 Dner3 1.561561e-09 0.6630562 0.812 0.619 3.829727e-05 Nb5d.INF
## 42 Cyyr13 2.378190e-09 0.7877871 0.310 0.101 5.832512e-05 Nb5d.INF
## 43 Scn3a2 3.010546e-09 0.7189504 0.731 0.544 7.383365e-05 Nb5d.INF
## 44 Fras1 6.568307e-09 0.7411780 0.196 0.038 1.610877e-04 Nb5d.INF
## 45 Dlc12 6.919855e-09 0.6193986 0.869 0.777 1.697094e-04 Nb5d.INF
## 46 Pcdh11x1 6.988003e-09 1.1498161 0.380 0.186 1.713808e-04 Nb5d.INF
## 47 Hnrnpll2 8.302118e-09 0.8569050 0.286 0.091 2.036094e-04 Nb5d.INF
## 48 Ptchd41 1.250139e-08 0.8004919 0.249 0.069 3.065965e-04 Nb5d.INF
## 49 Dmd1 1.653224e-08 0.6144068 0.890 0.780 4.054531e-04 Nb5d.INF
## 50 Antxr22 6.014578e-08 0.8743540 0.433 0.239 1.475075e-03 Nb5d.INF
## 51 Tll12 1.475759e-07 0.7194884 0.159 0.028 3.619298e-03 Nb5d.INF
## 52 Gucy1b11 2.667132e-07 0.7414708 0.371 0.170 6.541140e-03 Nb5d.INF
## gene intAnno2
## 1 Dgkb IPAN2
## 2 4930428E07Rik IPAN2
## 3 Ctnna3 IPAN2
## 4 Sema5a IPAN2
## 5 Gm15261 IPAN2
## 6 1700024B18Rik IPAN2
## 7 Cdh9 IPAN2
## 8 Gm16226 IPAN2
## 9 Gm11339 IPAN2
## 10 Fam155a IPAN2
## 11 Large1 IPAN2
## 12 Gm30613 IPAN2
## 13 Nrxn3 IPAN2
## 14 Pcsk2 IPAN2
## 15 Gm21847 IPAN2
## 16 Ptprz1 IPAN2
## 17 Kcnd2 IPAN2
## 18 Alk IPAN2
## 19 Calcb IPAN2
## 20 Cacnb4 IPAN2
## 21 Rab27b IPAN2
## 22 Ppm1h IPAN2
## 23 Tmcc3 IPAN2
## 24 Gm21798 IPAN2
## 25 Trpc3 IPAN2
## 26 Arid5b IPAN2
## 27 Nell1 IPAN2
## 28 Dclk1 IPAN2
## 29 Oxr1 IPAN2
## 30 Sema3c IPAN2
## 31 Gm30094 IPAN2
## 32 Adcy8 IPAN2
## 33 Kcnq5 IPAN2
## 34 Pcsk2os2 IPAN2
## 35 Rgs7 IPAN2
## 36 Asxl3 IPAN2
## 37 Nkain2 IPAN2
## 38 Csmd1 IPAN2
## 39 Necab1 IPAN2
## 40 Ppp1r12b IPAN2
## 41 Dner IPAN2
## 42 Cyyr1 IPAN2
## 43 Scn3a IPAN2
## 44 Fras1 IPAN2
## 45 Dlc1 IPAN2
## 46 Pcdh11x IPAN2
## 47 Hnrnpll IPAN2
## 48 Ptchd4 IPAN2
## 49 Dmd IPAN2
## 50 Antxr2 IPAN2
## 51 Tll1 IPAN2
## 52 Gucy1b1 IPAN2#pp.stat.DEG <- list()pp.stat.DEG[[2]] <- df_test1.DEGs_new %>% filter(p_val_adj < cut.padj &
abs(avg_log2FC) > cut.log2FC &
pct.1 > cut.pct1) %>%
group_by(intAnno2,cluster) %>%
summarise(up.DEGs = n()) %>% as.data.frame() %>%
ggplot(aes(x=intAnno2, y=up.DEGs, color = cluster)) +
geom_bar(stat="summary", fun="mean", position = position_dodge(0.75), width = 0.58, fill="white") +
theme_classic(base_size = 15) +
scale_color_manual(values = color.test1, name="") +
labs(title=paste0("up.DEGs stat, pct.1>",cut.pct1,", padj<",cut.padj,", |log2FC|>","log2(1.5)"), y = "Proportion") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6),
title =element_text(size=12, face='bold'))## `summarise()` has grouped output by 'intAnno2'. You can override using the
## `.groups` argument.pp.stat.DEG[[2]]
DEGs.IPAN1 <- (df_test1.DEGs_new %>% filter(p_val_adj < cut.padj &
abs(avg_log2FC) > cut.log2FC &
pct.1 > cut.pct1) %>%
group_by(intAnno2,cluster) %>% filter ( intAnno2 == "IPAN1") %>% as.data.frame())$gene
DEGs.IPAN2 <- (df_test1.DEGs_new %>% filter(p_val_adj < cut.padj &
abs(avg_log2FC) > cut.log2FC &
pct.1 > cut.pct1) %>%
group_by(intAnno2,cluster) %>% filter ( intAnno2 == "IPAN2") %>% as.data.frame())$genepp.cut2.IPAN1 <- DoHeatmap(subset(test1.seur, subset = intAnno1 == "IPAN1"), slot = "scale.data", disp.min = -1, disp.max = 2,
features = DEGs.IPAN1[c(20:113,1:19)], group.by = "cnt1",
group.colors = color.test1) + guides(color=FALSE) + theme(axis.text.y = element_text(size=2.35),
plot.margin = unit(c(0.3,0.3,0.3,0.3),"cm")) +
labs(title = "cut2, IPAN1")
pp.cut2.IPAN1
pp.cut2.IPAN2 <- DoHeatmap(subset(test1.seur, subset = intAnno1 == "IPAN2"), slot = "scale.data", disp.min = -1, disp.max = 2,
features = DEGs.IPAN2[c(10:52,1:9)], group.by = "cnt1",
group.colors = color.test1) + guides(color=FALSE) + theme(axis.text.y = element_text(size = 2.35),
plot.margin = unit(c(0.3,0.3,0.3,0.3),"cm")) +
labs(title = "cut2, IPAN2")
pp.cut2.IPAN2
markers
check.plot <- c("Il13ra1","Il4ra","Calca","Calcb",
"Nmu","Chat")
vln_df <- data.frame(test1.seur@meta.data,
t(test1.seur@assays$RNA@data[check.plot,]))
head(vln_df)## orig.ident nCount_RNA nFeature_RNA percent.mt percent.rb
## AAACCCACAAGACGAC-1_1 Nb5d.PBS_INF 3257 1801 0.36843721 0.3991403
## AAACCCAGTGGGCTCT-1_1 Nb5d.PBS_INF 1511 997 0.66181337 0.4632694
## AAACCCAGTTTGTTCT-1_1 Nb5d.PBS_INF 2855 1577 0.98073555 0.3152364
## AAACCCATCCTAGCCT-1_1 Nb5d.PBS_INF 2433 1451 0.08220304 0.3699137
## AAACCCATCGAAACAA-1_1 Nb5d.PBS_INF 3129 1656 0.12783637 0.4474273
## AAACCCATCGGTCAGC-1_1 Nb5d.PBS_INF 2201 1294 0.22716947 0.2271695
## S.Score G2M.Score Phase cnt rep newAnno
## AAACCCACAAGACGAC-1_1 0.011590405 -0.0004169865 S Nb5d.INF rep4 EMN3
## AAACCCAGTGGGCTCT-1_1 -0.024203070 0.0012414826 G2M Nb5d.PBS rep4 IPAN1
## AAACCCAGTTTGTTCT-1_1 -0.013980476 0.0039329410 G2M Nb5d.INF rep1 EMN3
## AAACCCATCCTAGCCT-1_1 -0.028925620 -0.0132582758 G1 Nb5d.INF rep2 EMN1
## AAACCCATCGAAACAA-1_1 -0.008077289 -0.0028336129 G1 Nb5d.PBS rep3 IPAN4
## AAACCCATCGGTCAGC-1_1 -0.023612751 0.0327239644 G2M Nb5d.PBS rep4 EMN1
## sample tissue nCount_SCT nFeature_SCT condition
## AAACCCACAAGACGAC-1_1 Nb5d.INF4 Ileum 2592 1794 INF_CTL
## AAACCCAGTGGGCTCT-1_1 Nb5d.PBS4 Ileum 1694 996 PBS_CTL
## AAACCCAGTTTGTTCT-1_1 Nb5d.INF1 Ileum 2495 1576 INF_CTL
## AAACCCATCCTAGCCT-1_1 Nb5d.INF2 Ileum 2324 1451 INF_CTL
## AAACCCATCGAAACAA-1_1 Nb5d.PBS3 Ileum 2552 1646 PBS_CTL
## AAACCCATCGGTCAGC-1_1 Nb5d.PBS4 Ileum 2171 1293 PBS_CTL
## seurat_clusters sort_clusters intAnno1 intAnno2
## AAACCCACAAGACGAC-1_1 11 11 EMN2 EMN2
## AAACCCAGTGGGCTCT-1_1 22 22 IPAN1 IPAN1.1
## AAACCCAGTTTGTTCT-1_1 11 11 EMN2 EMN2
## AAACCCATCCTAGCCT-1_1 4 4 EMN1 EMN1
## AAACCCATCGAAACAA-1_1 19 19 IPAN4 IPAN4
## AAACCCATCGGTCAGC-1_1 8 8 EMN1 EMN1
## score.EMN1 score.EMN2 score.EMN3 score.EMN4
## AAACCCACAAGACGAC-1_1 0.07919591 0.241706810 0.27217296 0.12854583
## AAACCCAGTGGGCTCT-1_1 -0.15104916 -0.182227557 -0.08972356 -0.02780619
## AAACCCAGTTTGTTCT-1_1 0.06398507 0.271974508 0.38593823 0.13952419
## AAACCCATCCTAGCCT-1_1 0.45628820 0.004121058 -0.09053160 -0.25144656
## AAACCCATCGAAACAA-1_1 -0.22677892 -0.176042364 0.17667288 0.07109063
## AAACCCATCGGTCAGC-1_1 0.41500886 0.078972206 -0.04352445 -0.01717643
## score.EMN5 score.IMN1 score.IMN2 score.IMN3
## AAACCCACAAGACGAC-1_1 0.112776596 -0.048743641 0.087677011 -0.06945631
## AAACCCAGTGGGCTCT-1_1 -0.078949105 -0.164458377 -0.010275168 0.03123894
## AAACCCAGTTTGTTCT-1_1 0.076261976 0.013262972 -0.086306052 -0.16199490
## AAACCCATCCTAGCCT-1_1 -0.014058236 -0.106028650 -0.055285442 -0.12949849
## AAACCCATCGAAACAA-1_1 0.102718840 -0.004683565 -0.006606094 -0.07936345
## AAACCCATCGGTCAGC-1_1 -0.008261381 -0.105501039 0.053248882 -0.05854380
## score.IMN4 score.IN1 score.IN2 score.IN3
## AAACCCACAAGACGAC-1_1 0.002799472 -0.052584879 -0.04337769 0.02522416
## AAACCCAGTGGGCTCT-1_1 -0.082036820 -0.107881694 -0.07353192 0.06210550
## AAACCCAGTTTGTTCT-1_1 -0.030034210 -0.109808107 -0.05886169 0.03389016
## AAACCCATCCTAGCCT-1_1 -0.079803316 -0.135613705 -0.12109194 0.16525651
## AAACCCATCGAAACAA-1_1 -0.014348463 -0.053893573 0.11275158 -0.04386948
## AAACCCATCGGTCAGC-1_1 -0.066043337 -0.004224746 0.01935024 0.07307944
## score.IPAN1.1 score.IPAN1.2 score.IPAN2.1 score.IPAN2.2
## AAACCCACAAGACGAC-1_1 -0.06921930 -0.05854091 -0.12090052 -0.03200085
## AAACCCAGTGGGCTCT-1_1 0.39874813 0.50626549 0.08046528 -0.05788911
## AAACCCAGTTTGTTCT-1_1 -0.10141645 -0.04456315 -0.04661481 0.01507260
## AAACCCATCCTAGCCT-1_1 -0.02942262 -0.11171721 -0.08567541 -0.03353428
## AAACCCATCGAAACAA-1_1 0.01651234 -0.01855972 0.11727520 0.26751667
## AAACCCATCGGTCAGC-1_1 -0.08079498 -0.09590834 -0.06355259 0.07461285
## score.IPAN3 score.IPAN4 score.INFxCTL_IPAN1
## AAACCCACAAGACGAC-1_1 0.009074399 -0.033702006 0.02559085
## AAACCCAGTGGGCTCT-1_1 0.075643417 -0.066791575 0.10998073
## AAACCCAGTTTGTTCT-1_1 0.023826742 0.025015471 -0.01209398
## AAACCCATCCTAGCCT-1_1 0.011699673 -0.003267128 0.03061715
## AAACCCATCGAAACAA-1_1 0.161399262 0.714055897 -0.02355308
## AAACCCATCGGTCAGC-1_1 -0.100991813 0.072239711 0.03672466
## score.INFxCTL_IPAN2 score.All_PBSup score.All_INFup
## AAACCCACAAGACGAC-1_1 0.03219975 0.038754467 0.14092349
## AAACCCAGTGGGCTCT-1_1 0.01085557 0.141496754 0.15347058
## AAACCCAGTTTGTTCT-1_1 0.07094068 0.042089530 0.11806283
## AAACCCATCCTAGCCT-1_1 -0.10919176 0.089116669 0.07064033
## AAACCCATCGAAACAA-1_1 0.10012150 -0.068982944 0.09493913
## AAACCCATCGGTCAGC-1_1 -0.13022208 0.005274842 0.09648734
## score.All_CTLup score.All_CKOup score.IPAN1_PBSup
## AAACCCACAAGACGAC-1_1 0.04699572 0.12328759 -0.07772672
## AAACCCAGTGGGCTCT-1_1 0.16455231 0.27409426 0.83477666
## AAACCCAGTTTGTTCT-1_1 0.02675111 0.04651514 -0.04362891
## AAACCCATCCTAGCCT-1_1 -0.06082137 0.17571025 -0.02777775
## AAACCCATCGAAACAA-1_1 -0.06550854 -0.04899024 0.16010606
## AAACCCATCGGTCAGC-1_1 -0.14775899 0.24252663 0.05487237
## score.IPAN1_INFup score.IPAN1_CTLup score.IPAN1_CKOup
## AAACCCACAAGACGAC-1_1 0.09536479 0.005898024 -0.08990901
## AAACCCAGTGGGCTCT-1_1 0.09832854 0.142199961 0.95544282
## AAACCCAGTTTGTTCT-1_1 0.05657979 -0.036408520 -0.07090544
## AAACCCATCCTAGCCT-1_1 0.02614203 0.014353383 -0.20984452
## AAACCCATCGAAACAA-1_1 0.04713297 -0.020370635 0.21988923
## AAACCCATCGGTCAGC-1_1 0.09156870 0.008539614 -0.00446080
## score.IPAN2_PBSup score.IPAN2_INFup score.IPAN2_CTLup
## AAACCCACAAGACGAC-1_1 0.23986616 0.09934860 0.03204373
## AAACCCAGTGGGCTCT-1_1 0.46980201 -0.02241394 0.03228071
## AAACCCAGTTTGTTCT-1_1 0.18824079 0.10546492 0.07251113
## AAACCCATCCTAGCCT-1_1 -0.12377840 -0.04855095 -0.09076901
## AAACCCATCGAAACAA-1_1 0.19893775 0.05686627 0.05049066
## AAACCCATCGGTCAGC-1_1 0.07972086 -0.01931504 -0.13585336
## score.IPAN2_CKOup score.IEGs cnt1 cnt2 Il13ra1 Il4ra
## AAACCCACAAGACGAC-1_1 -0.30729036 0.13012508 INF EMN2.INF 0 0
## AAACCCAGTGGGCTCT-1_1 0.58710490 0.01972603 PBS IPAN1.PBS 0 0
## AAACCCAGTTTGTTCT-1_1 -0.29511538 -0.01351550 INF EMN2.INF 0 0
## AAACCCATCCTAGCCT-1_1 -0.43075122 -0.02168908 INF EMN1.INF 0 0
## AAACCCATCGAAACAA-1_1 -0.14695600 0.01547855 PBS IPAN4.PBS 0 0
## AAACCCATCGGTCAGC-1_1 -0.02570226 -0.01439351 PBS EMN1.PBS 0 0
## Calca Calcb Nmu Chat
## AAACCCACAAGACGAC-1_1 0 0.000000 0.000000 0.000000
## AAACCCAGTGGGCTCT-1_1 0 2.030531 2.030531 0.000000
## AAACCCAGTTTGTTCT-1_1 0 0.000000 0.000000 0.000000
## AAACCCATCCTAGCCT-1_1 0 0.000000 0.000000 1.631229
## AAACCCATCGAAACAA-1_1 0 0.000000 0.000000 0.000000
## AAACCCATCGGTCAGC-1_1 0 0.000000 0.000000 0.000000pp.vln <- list()
pp.vln <- lapply(1:5, function(jj){
pp.tmp <- ggplot(vln_df, aes(x = cnt2, y= get(check.plot[jj]), fill = cnt1)) +
geom_violin(trim = TRUE, scale = 'width', lwd=0.1) +
labs(x="",y="",title=check.plot[jj]) + NoLegend() +
#scale_fill_manual(values = ggsci::pal_d3("category20c")(20)[c(1,2)]) +
scale_fill_manual(values = color.test1) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line = element_line(color = "black", size = 0.1),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.03,0.1,0,0.1),"cm")) + coord_cartesian(ylim=c(0,4.35)) + geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1) +
stat_summary(fun.y=mean, geom="point", shape=23, size=0.65, color="black", fill="white", alpha=0.75)
})## Warning: `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.pp.vln[[6]] <- ggplot(vln_df, aes(x = cnt2, y= get(check.plot[6]), fill = cnt1)) +
geom_violin(trim = TRUE, scale = 'width', lwd=0.1) +
labs(x="",y="",title=check.plot[6]) + NoLegend() +
#scale_fill_manual(values = ggsci::pal_d3("category20c")(20)[c(1,2)]) +
scale_fill_manual(values = color.test1) +
theme(axis.title.x = element_blank(),
#axis.text.x = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 8.1),
#axis.ticks.x = element_blank(),
axis.ticks.x = element_line(color = "black",size=0.05),
axis.line = element_line(color = "black", size = 0.1),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.03,0.1,0,0.1),"cm")) + coord_cartesian(ylim=c(0,4.35)) + geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1) +
stat_summary(fun.y=mean, geom="point", shape=23, size=0.65, color="black", fill="white", alpha=0.75)## Warning: `fun.y` is deprecated. Use `fun` instead.cowplot::plot_grid(
plotlist = pp.vln,
ncol = 1,
rel_heights = c(rep(1,5),1.55))
pp.vln.s <- list()
pp.vln.s <- lapply(1:5, function(jj){
pp.tmp <- ggplot(vln_df, aes(x = cnt2, y= get(check.plot[jj]), fill = cnt1)) +
geom_violin(trim = TRUE, scale = 'width', lwd=0.1) +
labs(x="",y="",title=check.plot[jj]) + NoLegend() +
#scale_fill_manual(values = ggsci::pal_d3("category20c")(20)[c(1,2)]) +
scale_fill_manual(values = color.test1) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line = element_line(color = "black", size = 0.1),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.03,0.1,0,0.1),"cm")) + coord_cartesian(ylim=c(0,4.35)) + geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1) +
stat_summary(fun.y=mean, geom="point", shape=23, size=0.45, color="black", fill="white", alpha=0.75) +
stat_compare_means(aes(label= ..p.signif..),
method = "wilcox.test",
comparisons = list.cnt2,
label.y = c(rep(3.75,16)),
size = 2.5)
})## Warning: `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.pp.vln.s[[6]] <- ggplot(vln_df, aes(x = cnt2, y= get(check.plot[6]), fill = cnt1)) +
geom_violin(trim = TRUE, scale = 'width', lwd=0.1) +
labs(x="",y="",title=check.plot[6]) + NoLegend() +
#scale_fill_manual(values = ggsci::pal_d3("category20c")(20)[c(1,2)]) +
scale_fill_manual(values = color.test1) +
theme(axis.title.x = element_blank(),
#axis.text.x = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 8.1),
#axis.ticks.x = element_blank(),
axis.ticks.x = element_line(color = "black",size=0.05),
axis.line = element_line(color = "black", size = 0.1),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.03,0.1,0,0.1),"cm")) + coord_cartesian(ylim=c(0,4.35)) + geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1) +
stat_summary(fun.y=mean, geom="point", shape=23, size=0.45, color="black", fill="white", alpha=0.75) +
stat_compare_means(aes(label= ..p.signif..),
method = "wilcox.test",
comparisons = list.cnt2,
label.y = c(rep(3.75,16)),
size = 2.5)## Warning: `fun.y` is deprecated. Use `fun` instead.cowplot::plot_grid(
plotlist = pp.vln.s,
ncol = 1,
rel_heights = c(rep(1,5),1.55))
pp.vln.a <- list()
pp.vln.a <- lapply(1:5, function(jj){
pp.tmp <- ggplot(vln_df, aes(x = intAnno1, y= get(check.plot[jj]), fill = intAnno1)) +
geom_violin(trim = TRUE, scale = 'width', lwd=0.1) +
labs(x="",y="",title=check.plot[jj]) + NoLegend() +
scale_fill_manual(values = color.A1) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line = element_line(color = "black", size = 0.1),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.03,0.1,0,0.1),"cm")) + coord_cartesian(ylim=c(0,4.35)) + geom_boxplot(outlier.size = 0, fill="white", width=0.12, size=0.1) +
stat_summary(fun.y=mean, geom="point", shape=23, size=0.45, color="black", fill="white", alpha=0.75)
})## Warning: `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.
## `fun.y` is deprecated. Use `fun` instead.pp.vln.a[[6]] <- ggplot(vln_df, aes(x = intAnno1, y= get(check.plot[6]), fill = intAnno1)) +
geom_violin(trim = TRUE, scale = 'width', lwd=0.1) +
labs(x="",y="",title=check.plot[6]) + NoLegend() +
scale_fill_manual(values = color.A1) +
theme(axis.title.x = element_blank(),
#axis.text.x = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 8.1),
#axis.ticks.x = element_blank(),
axis.ticks.x = element_line(color = "black",size=0.05),
axis.line = element_line(color = "black", size = 0.1),
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
plot.margin = unit(c(0.03,0.1,0,0.1),"cm")) + coord_cartesian(ylim=c(0,4.35)) + geom_boxplot(outlier.size = 0, fill="white", width=0.12, size=0.1) +
stat_summary(fun.y=mean, geom="point", shape=23, size=0.45, color="black", fill="white", alpha=0.75)## Warning: `fun.y` is deprecated. Use `fun` instead.cowplot::plot_grid(
plotlist = pp.vln.a,
ncol = 1,
rel_heights = c(rep(1,5),1.4))
FeaturePlot(test1.seur, features = check.plot, ncol = 3)
FeaturePlot(subset(test1.seur, subset = cnt1 == "PBS"), features = check.plot, ncol = 3)
FeaturePlot(test1.seur, features = check.plot, ncol = 3, pt.size = 1)
FeaturePlot(subset(test1.seur, subset = cnt1 == "PBS"), features = check.plot, ncol = 3, pt.size = 1)
FeaturePlot(test1.seur, features = check.plot, ncol = 3, pt.size = 2)
FeaturePlot(subset(test1.seur, subset = cnt1 == "PBS"), features = check.plot, ncol = 3, pt.size = 2)
DotPlot(test1.seur, features = rev(check.plot), group.by = "cnt2") + coord_flip() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6))
DotPlot(test1.seur, features = rev(check.plot), group.by = "cnt2", cols = c("midnightblue","darkorange1")) + coord_flip() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6))
violin
pp.IPAN1 <- list()pp.IPAN1[["Nmu"]] <- VlnPlot(subset(test1.seur,subset=intAnno1 %in% c("IPAN1")), assay = "RNA", features = c("Nmu"), group.by = "cnt", ncol = 1, cols = color.test1, pt.size = 0.01,
combine = T) +
geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black", alpha=0.55) + coord_cartesian(ylim=c(0,5)) + ggpubr::stat_compare_means(aes(lable = ..p.signif..),
method = "wilcox.test",
comparisons = list(c("Nb5d.PBS","Nb5d.INF")),
label.y = c(4.35),
size=3.5
) + NoLegend()
pp.IPAN1[["Nmu"]]
pp.IPAN1[["Calcb"]] <- VlnPlot(subset(test1.seur,subset=intAnno1 %in% c("IPAN1")), assay = "RNA", features = c("Calcb"), group.by = "cnt", ncol = 1, cols = color.test1, pt.size = 0.01,
combine = T) +
geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black", alpha=0.55) + coord_cartesian(ylim=c(0,5)) + ggpubr::stat_compare_means(aes(lable = ..p.signif..),
method = "wilcox.test",
comparisons = list(c("Nb5d.PBS","Nb5d.INF")),
label.y = c(4.05),
size=3.5
) + NoLegend()
pp.IPAN1[["Calcb"]]
pp.IPAN1[["Calca"]] <- VlnPlot(subset(test1.seur,subset=intAnno1 %in% c("IPAN1")), assay = "RNA", features = c("Calca"), group.by = "cnt", ncol = 1, cols = color.test1, pt.size = 0,
combine = T) +
#geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) +
geom_point(size=0.3) +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black", alpha=0.55) + coord_cartesian(ylim=c(0,5)) + ggpubr::stat_compare_means(aes(lable = ..p.signif..),
method = "wilcox.test",
comparisons = list(c("Nb5d.PBS","Nb5d.INF")),
label.y = c(2.15),
size=3.5
) + NoLegend()
pp.IPAN1[["Calca"]]
violin-Il13ra1+
ppi.IPAN1 <- list()test1.seur$Il13ra1p <- test1.seur@assays$RNA@data["Il13ra1",] >0ppi.IPAN1[["Nmu"]] <- VlnPlot(subset(test1.seur,subset=intAnno1 %in% c("IPAN1") & Il13ra1p==TRUE), assay = "RNA", features = c("Nmu"), group.by = "cnt", ncol = 1, cols = color.test1, pt.size = 0.01,
combine = T) +
geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black", alpha=0.55) + coord_cartesian(ylim=c(0,5)) + ggpubr::stat_compare_means(aes(lable = ..p.signif..),
method = "wilcox.test",
comparisons = list(c("Nb5d.PBS","Nb5d.INF")),
label.y = c(4.05),
size=3.5
) + NoLegend()
ppi.IPAN1[["Nmu"]]
ppi.IPAN1[["Calcb"]] <- VlnPlot(subset(test1.seur,subset=intAnno1 %in% c("IPAN1") & Il13ra1p==TRUE), assay = "RNA", features = c("Calcb"), group.by = "cnt", ncol = 1, cols = color.test1, pt.size = 0.01,
combine = T) +
geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black", alpha=0.55) + coord_cartesian(ylim=c(0,5)) + ggpubr::stat_compare_means(aes(lable = ..p.signif..),
method = "wilcox.test",
comparisons = list(c("Nb5d.PBS","Nb5d.INF")),
label.y = c(3.6),
size=3.5
) + NoLegend()
ppi.IPAN1[["Calcb"]]
ppi.IPAN1[["Calca"]] <- VlnPlot(subset(test1.seur,subset=intAnno1 %in% c("IPAN1") & Il13ra1p==TRUE), assay = "RNA", features = c("Calca"), group.by = "cnt", ncol = 1, cols = color.test1, pt.size = 0,
combine = T) +
#geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) +
geom_point(size=0.3) +
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black", alpha=0.55) + coord_cartesian(ylim=c(0,5)) + ggpubr::stat_compare_means(aes(lable = ..p.signif..),
method = "wilcox.test",
comparisons = list(c("Nb5d.PBS","Nb5d.INF")),
label.y = c(2.15),
size=3.5
) + NoLegend()
ppi.IPAN1[["Calca"]]
check ligand-receptor
using markers in batch PBS_INF
#Idents(test1.seur) <- "intAnno1"
#test1.markers.pre <- FindAllMarkers(test1.seur, only.pos = TRUE, min.pct = 0.05,
# assay = "RNA",
# test.use = "MAST",
# logfc.threshold = 0.25)
#test1.markers.pre <- read.table("Baf53cre_Nb.markers_intAnno1.PBSvsINF.csv", header = TRUE, sep = ",")
#test1.markers.pre %>% group_by(cluster) %>% top_n(n = 8, wt = avg_log2FC)directly using markers in SCT_intAnno1
markers.new <- read.csv("../integration_Nb5d/Baf53cre_Nb.markers.SCT_intAnno1.202402.csv")
markers.new$cluster <- factor(as.character(markers.new$cluster),
levels = levels(GEX.seur$intAnno1))
head(markers.new)## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## 1 0 1.824018 0.953 0.335 0 EMN1 Ptprt
## 2 0 1.668551 0.981 0.495 0 EMN1 Tshz2
## 3 0 1.585436 0.938 0.319 0 EMN1 Bnc2
## 4 0 1.384775 0.909 0.397 0 EMN1 Grik1
## 5 0 1.301007 1.000 0.793 0 EMN1 Rbfox1
## 6 0 1.107801 0.992 0.864 0 EMN1 Negr1CellChat
CellChat.secreting <- list(ligand=as.vector(unlist(read.table("./figures.integration/PBSvsINF.replot/Markers/CellChat.list/CellChat.interaction.secreting.ligand_all.txt"))),
receptor=as.vector(unlist(read.table("./figures.integration/PBSvsINF.replot/Markers/CellChat.list/CellChat.interaction.secreting.receptor_all.txt"))))lapply(CellChat.secreting,length)## $ligand
## [1] 364
##
## $receptor
## [1] 328lapply(CellChat.secreting,head)## $ligand
## [1] "Tgfb1" "Tgfb2" "Tgfb3" "Bmp2" "Bmp4" "Gdf5"
##
## $receptor
## [1] "Tgfbr1" "Tgfbr2" "Acvr1b" "Acvr1c" "Acvr1" "Bmpr1a"markers
# sort all ligand genes in significant markers
markers.new_ligand.pct_0.15.padj_0.001 <- (markers.new %>% group_by(cluster) %>%
filter(pct.1>0.15 & p_val_adj < 0.001 & gene %in% CellChat.secreting$ligand) %>%
#top_n(n = 48, wt = avg_log2FC) %>%
ungroup() %>%
arrange(desc(avg_log2FC*pct.1),gene) %>%
distinct(gene, .keep_all = TRUE) %>%
arrange(cluster,p_val_adj)) %>% as.data.frame()
# sort all receptor genes in significant markers
markers.new_receptor.pct_0.15.padj_0.001 <- (markers.new %>% group_by(cluster) %>%
filter(pct.1>0.15 & p_val_adj < 0.001 & gene %in% CellChat.secreting$receptor) %>%
#top_n(n = 48, wt = avg_log2FC) %>%
ungroup() %>%
arrange(desc(avg_log2FC*pct.1),gene) %>%
distinct(gene, .keep_all = TRUE) %>%
arrange(cluster,p_val_adj)) %>% as.data.frame()dim(markers.new_ligand.pct_0.15.padj_0.001)[1]## [1] 25dim(markers.new_receptor.pct_0.15.padj_0.001)[1]## [1] 63markers.new_ligand.pct_0.15.padj_0.001## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## 1 0.000000e+00 1.1076947 1.000 0.930 0.000000e+00 EMN1 Nrg3
## 2 6.065518e-184 0.5636193 0.549 0.228 1.239428e-179 EMN3 Eda
## 3 3.827394e-13 0.1322432 0.343 0.275 7.820898e-09 EMN3 Psap
## 4 9.105471e-59 0.5506514 0.526 0.153 1.860612e-54 EMN4 Hgf
## 5 4.624466e-275 1.5727641 0.858 0.250 9.449635e-271 EMN5 Tac1
## 6 1.827560e-252 1.1479185 0.626 0.047 3.734436e-248 EMN5 Penk
## 7 4.088367e-130 0.6924884 0.371 0.029 8.354170e-126 EMN5 Sema3e
## 8 3.521177e-45 0.4604326 0.478 0.164 7.195173e-41 EMN5 Ptn
## 9 0.000000e+00 0.5626011 0.389 0.073 0.000000e+00 IMN1 Kitl
## 10 6.181438e-279 1.2947562 0.751 0.133 1.263115e-274 IMN3 Vip
## 11 1.980605e-42 0.2330640 0.161 0.020 4.047167e-38 IMN3 Angpt1
## 12 4.458289e-42 0.3509201 0.406 0.148 9.110068e-38 IMN3 Gas6
## 13 5.582472e-16 0.1704251 0.435 0.266 1.140722e-11 IMN3 Fgf1
## 14 3.641857e-76 0.2957764 0.197 0.005 7.441771e-72 IMN4 Pdyn
## 15 2.671636e-47 0.3880091 0.255 0.035 5.459222e-43 IMN4 Grp
## 16 0.000000e+00 2.2246127 0.988 0.440 0.000000e+00 IN1 Nrg1
## 17 0.000000e+00 2.0988727 0.885 0.257 0.000000e+00 IN1 Gal
## 18 3.865699e-119 1.4448321 0.650 0.015 7.899170e-115 IN3 Sst
## 19 0.000000e+00 2.1177143 0.703 0.104 0.000000e+00 IPAN1 Nmu
## 20 0.000000e+00 1.1291159 0.658 0.080 0.000000e+00 IPAN1 Calcb
## 21 6.754990e-293 0.3905187 0.283 0.018 1.380315e-288 IPAN1 Il7
## 22 2.085811e-218 0.2727499 0.206 0.011 4.262146e-214 IPAN1 Bmp4
## 23 1.693378e-87 0.3077026 0.384 0.161 3.460248e-83 IPAN1 Nrg2
## 24 2.830143e-10 0.2201720 0.192 0.034 5.783115e-06 IPAN3 Sema3c
## 25 7.195690e-44 0.3025856 0.279 0.078 1.470367e-39 IPAN4 Vegfamarkers.new_receptor.pct_0.15.padj_0.001## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## 1 0.000000e+00 0.5207381 0.483 0.202 0.000000e+00 EMN1 Chrna7
## 2 0.000000e+00 0.4537600 0.360 0.093 0.000000e+00 EMN1 Oprk1
## 3 1.217599e-137 0.2181927 0.222 0.087 2.488041e-133 EMN1 Itga6
## 4 5.993268e-118 0.2926601 0.782 0.715 1.224664e-113 EMN1 Fgfr2
## 5 1.025052e-82 0.2338149 0.219 0.067 2.094592e-78 EMN3 Agtr1a
## 6 1.424846e-193 1.0748133 0.710 0.107 2.911530e-189 EMN4 Ntrk2
## 7 1.309460e-196 0.6894317 0.449 0.012 2.675751e-192 EMN5 Egfr
## 8 1.299446e-133 0.9245781 0.753 0.192 2.655289e-129 EMN5 Ptprz1
## 9 1.479486e-104 0.8215978 0.788 0.298 3.023181e-100 EMN5 Nrp2
## 10 6.782722e-208 0.3996465 0.423 0.192 1.385981e-203 IMN1 Igf2r
## 11 7.483520e-171 0.3270215 0.354 0.154 1.529182e-166 IMN1 Oprd1
## 12 6.710387e-174 0.9432587 0.813 0.220 1.371200e-169 IMN3 Gfra1
## 13 1.446881e-55 0.4236405 0.457 0.153 2.956557e-51 IMN3 Itga8
## 14 5.183468e-55 0.3684805 0.324 0.080 1.059190e-50 IMN3 Bdkrb2
## 15 7.268367e-51 0.2674485 0.213 0.032 1.485218e-46 IMN3 Npy2r
## 16 3.556710e-43 0.2414330 0.195 0.033 7.267780e-39 IMN3 F2r
## 17 2.409844e-33 0.2503877 0.266 0.079 4.924274e-29 IMN3 Tyro3
## 18 2.886256e-21 0.2248048 0.292 0.126 5.897776e-17 IMN3 Fzd3
## 19 5.592817e-13 0.1451864 0.352 0.213 1.142836e-08 IMN3 Chrna3
## 20 4.568093e-12 0.2013487 0.416 0.266 9.334442e-08 IMN3 Insr
## 21 5.477034e-12 0.1614192 0.308 0.175 1.119177e-07 IMN3 Adcyap1r1
## 22 3.132961e-08 0.1036473 0.163 0.082 6.401893e-04 IMN3 Sdc2
## 23 6.066951e-51 0.2319856 0.173 0.008 1.239721e-46 IMN4 Tacr3
## 24 2.583724e-08 0.1577374 0.242 0.123 5.279583e-04 IMN4 Chrnb4
## 25 2.601934e-188 0.9101337 0.729 0.258 5.316792e-184 IN1 Cdh11
## 26 1.869240e-30 0.2308660 0.285 0.113 3.819605e-26 IN1 Sort1
## 27 1.875902e-24 0.1618972 0.155 0.045 3.833219e-20 IN1 Ednrb
## 28 4.318443e-155 0.5486427 0.374 0.011 8.824307e-151 IN2 Sctr
## 29 9.501281e-79 0.2848264 0.212 0.008 1.941492e-74 IN2 Ntsr1
## 30 1.019314e-70 0.4843119 0.382 0.064 2.082867e-66 IN2 Nrp1
## 31 1.260777e-19 0.3588583 0.272 0.027 2.576272e-15 IN3 Vipr2
## 32 5.864233e-16 0.5714126 0.553 0.201 1.198297e-11 IN3 Gfra2
## 33 2.349976e-13 0.2554309 0.175 0.015 4.801941e-09 IN3 Tacr1
## 34 0.000000e+00 2.3298186 0.989 0.187 0.000000e+00 IPAN1 Ntrk3
## 35 0.000000e+00 1.7609063 0.946 0.204 0.000000e+00 IPAN1 Plxna4
## 36 0.000000e+00 1.1113411 0.590 0.009 0.000000e+00 IPAN1 Itgb6
## 37 0.000000e+00 0.7768492 0.553 0.044 0.000000e+00 IPAN1 Galr1
## 38 0.000000e+00 0.6517939 0.486 0.096 0.000000e+00 IPAN1 Ngfr
## 39 0.000000e+00 0.5432981 0.373 0.020 0.000000e+00 IPAN1 Met
## 40 4.699070e-282 0.7838060 0.834 0.547 9.602079e-278 IPAN1 Igf1r
## 41 2.664584e-189 0.5616217 0.554 0.199 5.444812e-185 IPAN1 Calcrl
## 42 1.052026e-186 0.4668295 0.481 0.164 2.149710e-182 IPAN1 Npr2
## 43 1.563327e-160 0.2500400 0.197 0.020 3.194502e-156 IPAN1 Gcgr
## 44 8.811416e-97 0.2263668 0.199 0.041 1.800525e-92 IPAN1 Il13ra1
## 45 2.064858e-59 0.1627802 0.162 0.044 4.219330e-55 IPAN1 Cntfr
## 46 1.329464e-52 0.2785298 0.393 0.213 2.716626e-48 IPAN1 Plxna2
## 47 1.907800e-41 0.2002669 0.356 0.198 3.898399e-37 IPAN1 Bmpr1a
## 48 7.062835e-31 0.1257900 0.168 0.073 1.443220e-26 IPAN1 Plxna3
## 49 1.271821e-26 0.1611509 0.311 0.188 2.598840e-22 IPAN1 Itgb1
## 50 5.474453e-26 0.1285295 0.208 0.108 1.118650e-21 IPAN1 Acvr1
## 51 9.130241e-24 0.1838976 0.434 0.309 1.865673e-19 IPAN1 Bmpr2
## 52 1.213296e-16 0.1025542 0.212 0.130 2.479249e-12 IPAN1 Lrp6
## 53 0.000000e+00 1.2980715 0.990 0.654 0.000000e+00 IPAN2 Alk
## 54 3.557226e-233 0.3622293 0.252 0.012 7.268835e-229 IPAN2 Nmur2
## 55 7.634807e-194 0.7217496 0.385 0.087 1.560096e-189 IPAN2 Ghr
## 56 1.622181e-27 0.7533781 0.642 0.259 3.314765e-23 IPAN3 Oprm1
## 57 0.000000e+00 1.1795053 0.629 0.014 0.000000e+00 IPAN4 Bmpr1b
## 58 2.367353e-88 0.2369190 0.157 0.003 4.837450e-84 IPAN4 Npy5r
## 59 6.581974e-72 0.2937324 0.191 0.015 1.344960e-67 IPAN4 Cckar
## 60 1.043326e-67 0.2705104 0.181 0.014 2.131933e-63 IPAN4 Glp1r
## 61 1.590873e-63 0.3124106 0.230 0.036 3.250790e-59 IPAN4 Npy1r
## 62 1.283549e-21 0.2569076 0.318 0.151 2.622804e-17 IPAN4 Fgfr1
## 63 2.412892e-16 0.2543119 0.507 0.332 4.930503e-12 IPAN4 Pard3PBSvsINF
DotPlot(test1.seur, features = markers.new_ligand.pct_0.15.padj_0.001$gene, group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 10.15))+ scale_y_discrete(limits=rev) + labs(title="ligand.pct_0.15.padj_0.001") +
scale_color_gradientn(colours = material.heat(100))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
DotPlot(test1.seur, features = markers.new_receptor.pct_0.15.padj_0.001$gene, group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 10))+ scale_y_discrete(limits=rev) + labs(title="receptor.pct_0.15.padj_0.001") +
scale_color_gradientn(colours = material.heat(100))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
NatNeur2021
DotPlot(ref.seur, features = markers.new_ligand.pct_0.15.padj_0.001$gene, group.by = "Anno2",
cols = c("midnightblue","darkorange1")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 10.15))+ scale_y_discrete(limits=rev) + labs(title="ligand.pct_0.15.padj_0.001") +
scale_color_gradientn(colours = material.heat(100))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
DotPlot(ref.seur, features = markers.new_receptor.pct_0.15.padj_0.001$gene, group.by = "Anno2",
cols = c("midnightblue","darkorange1")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 10))+ scale_y_discrete(limits=rev) + labs(title="receptor.pct_0.15.padj_0.001") +
scale_color_gradientn(colours = material.heat(100))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
CellTalkDB
mat.lr <- read.table("figures.integration/PBSvsINF.replot/Markers/CellTalkDB.list/CellTalkDB.20230203.mouse_lr_pair.txt", header = T)
head(mat.lr)## lr_pair ligand_gene_symbol receptor_gene_symbol ligand_gene_id
## 1 Wnt3_Fzd6 Wnt3 Fzd6 22415
## 2 Wnt3_Egfr Wnt3 Egfr 22415
## 3 Wnt3_Fzd2 Wnt3 Fzd2 22415
## 4 Wnt3_Lrp5 Wnt3 Lrp5 22415
## 5 Wnt3_Fzd1 Wnt3 Fzd1 22415
## 6 Wnt3_Fzd3 Wnt3 Fzd3 22415
## receptor_gene_id ligand_ensembl_protein_id receptor_ensembl_protein_id
## 1 14368 ENSMUSP00000000127 ENSMUSP00000022906
## 2 13649 ENSMUSP00000000127 ENSMUSP00000020329
## 3 57265 ENSMUSP00000000127 ENSMUSP00000091463
## 4 16973 ENSMUSP00000000127 ENSMUSP00000025856
## 5 14362 ENSMUSP00000000127 ENSMUSP00000058629
## 6 14365 ENSMUSP00000000127 ENSMUSP00000115325
## ligand_ensembl_gene_id receptor_ensembl_gene_id evidence
## 1 ENSMUSG00000000125 ENSMUSG00000022297 19901330
## 2 ENSMUSG00000000125 ENSMUSG00000020122 17374561
## 3 ENSMUSG00000000125 ENSMUSG00000050288 31907106
## 4 ENSMUSG00000000125 ENSMUSG00000024913 11719191
## 5 ENSMUSG00000000125 ENSMUSG00000044674 20667980
## 6 ENSMUSG00000000125 ENSMUSG00000007989 18212053length(unique(mat.lr$ligand_gene_symbol))## [1] 651length(unique(mat.lr$receptor_gene_symbol))## [1] 588markers
# sort all ligand genes in significant markers
markers.new_ligand.pct_0.15.padj_0.001 <- (markers.new %>% group_by(cluster) %>%
filter(pct.1>0.15 & p_val_adj < 0.001 & gene %in% unique(mat.lr$ligand_gene_symbol)) %>%
#top_n(n = 48, wt = avg_log2FC) %>%
ungroup() %>%
arrange(desc(avg_log2FC*pct.1),gene) %>%
distinct(gene, .keep_all = TRUE) %>%
arrange(cluster,p_val_adj)) %>% as.data.frame()
# sort all receptor genes in significant markers
markers.new_receptor.pct_0.15.padj_0.001 <- (markers.new %>% group_by(cluster) %>%
filter(pct.1>0.15 & p_val_adj < 0.001 & gene %in% unique(mat.lr$receptor_gene_symbol)) %>%
#top_n(n = 48, wt = avg_log2FC) %>%
ungroup() %>%
arrange(desc(avg_log2FC*pct.1),gene) %>%
distinct(gene, .keep_all = TRUE) %>%
arrange(cluster,p_val_adj)) %>% as.data.frame()dim(markers.new_ligand.pct_0.15.padj_0.001)[1]## [1] 89dim(markers.new_receptor.pct_0.15.padj_0.001)[1]## [1] 134markers.new_ligand.pct_0.15.padj_0.001## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## 1 0.000000e+00 1.1076947 1.000 0.930 0.000000e+00 EMN1 Nrg3
## 2 0.000000e+00 0.7232543 0.822 0.558 0.000000e+00 EMN1 Dscam
## 3 0.000000e+00 0.5769797 0.926 0.766 0.000000e+00 EMN1 Sema6d
## 4 4.695799e-256 0.5021931 0.427 0.218 9.595396e-252 EMN1 Rspo2
## 5 4.627980e-92 0.2517767 0.511 0.387 9.456815e-88 EMN1 Rgmb
## 6 1.405155e-138 0.3616922 0.366 0.117 2.871293e-134 EMN2 Colq
## 7 1.123065e-108 0.2768502 0.324 0.110 2.294870e-104 EMN2 Slit1
## 8 0.000000e+00 0.8797513 0.494 0.046 0.000000e+00 EMN3 Nxph1
## 9 6.065518e-184 0.5636193 0.549 0.228 1.239428e-179 EMN3 Eda
## 10 3.827394e-13 0.1322432 0.343 0.275 7.820898e-09 EMN3 Psap
## 11 2.501708e-68 0.4546573 0.267 0.021 5.111989e-64 EMN4 Lama2
## 12 9.105471e-59 0.5506514 0.526 0.153 1.860612e-54 EMN4 Hgf
## 13 6.111817e-42 0.2823764 0.224 0.030 1.248889e-37 EMN4 Efnb2
## 14 4.138119e-39 0.4284888 0.511 0.198 8.455832e-35 EMN4 Cd200
## 15 5.410714e-10 0.1619891 0.205 0.090 1.105625e-05 EMN4 Vcl
## 16 4.624466e-275 1.5727641 0.858 0.250 9.449635e-271 EMN5 Tac1
## 17 4.088367e-130 0.6924884 0.371 0.029 8.354170e-126 EMN5 Sema3e
## 18 2.388526e-117 0.4307895 0.288 0.009 4.880715e-113 EMN5 Ntn1
## 19 3.521177e-45 0.4604326 0.478 0.164 7.195173e-41 EMN5 Ptn
## 20 9.423596e-18 0.3282928 0.898 0.778 1.925618e-13 EMN5 Ncam1
## 21 1.089338e-13 0.2299454 0.315 0.155 2.225954e-09 EMN5 Agrp
## 22 1.000216e-08 0.1727080 0.272 0.151 2.043841e-04 EMN5 Jam3
## 23 1.205417e-08 0.2263650 0.435 0.296 2.463149e-04 EMN5 Calm1
## 24 1.611949e-08 0.1669165 0.306 0.177 3.293856e-04 EMN5 Gng12
## 25 0.000000e+00 0.5626011 0.389 0.073 0.000000e+00 IMN1 Kitl
## 26 1.411700e-171 0.3816347 0.473 0.254 2.884667e-167 IMN1 Spon1
## 27 1.675348e-71 0.2812034 0.552 0.424 3.423406e-67 IMN1 Slit3
## 28 6.181438e-279 1.2947562 0.751 0.133 1.263115e-274 IMN3 Vip
## 29 1.251052e-88 0.7199983 0.948 0.636 2.556400e-84 IMN3 Slit2
## 30 1.980605e-42 0.2330640 0.161 0.020 4.047167e-38 IMN3 Angpt1
## 31 4.458289e-42 0.3509201 0.406 0.148 9.110068e-38 IMN3 Gas6
## 32 5.525018e-24 0.2773511 0.404 0.200 1.128982e-19 IMN3 Dkk3
## 33 1.438444e-18 0.2566154 0.539 0.340 2.939316e-14 IMN3 Afdn
## 34 1.495309e-17 0.1574750 0.175 0.060 3.055514e-13 IMN3 Adamts3
## 35 9.323012e-17 0.2009966 0.408 0.233 1.905064e-12 IMN3 Sema4d
## 36 5.582472e-16 0.1704251 0.435 0.266 1.140722e-11 IMN3 Fgf1
## 37 1.597041e-89 0.6218592 0.455 0.060 3.263393e-85 IMN4 Adam12
## 38 3.567328e-76 0.4609484 0.388 0.049 7.289479e-72 IMN4 Lama4
## 39 3.641857e-76 0.2957764 0.197 0.005 7.441771e-72 IMN4 Pdyn
## 40 3.442470e-63 0.4258319 0.315 0.037 7.034344e-59 IMN4 Igfbp5
## 41 2.790880e-52 0.5376480 0.473 0.144 5.702884e-48 IMN4 Sema6a
## 42 2.671636e-47 0.3880091 0.255 0.035 5.459222e-43 IMN4 Grp
## 43 6.849351e-19 0.2384707 0.294 0.108 1.399596e-14 IMN4 St6gal1
## 44 1.062254e-14 0.1768879 0.170 0.048 2.170610e-10 IMN4 Rspo3
## 45 7.036385e-14 0.2682218 0.442 0.251 1.437815e-09 IMN4 Fyn
## 46 0.000000e+00 2.2246127 0.988 0.440 0.000000e+00 IN1 Nrg1
## 47 0.000000e+00 2.0988727 0.885 0.257 0.000000e+00 IN1 Gal
## 48 0.000000e+00 1.1638942 0.891 0.415 0.000000e+00 IN1 Fgf12
## 49 0.000000e+00 0.8357698 0.546 0.030 0.000000e+00 IN1 Col18a1
## 50 4.418156e-72 0.2408625 0.178 0.016 9.028059e-68 IN1 Cntn6
## 51 2.022553e-237 1.2472501 0.973 0.665 4.132884e-233 IN2 Efna5
## 52 1.940703e-76 0.5556081 0.517 0.130 3.965633e-72 IN2 Col4a2
## 53 2.074089e-68 0.4670462 0.401 0.082 4.238193e-64 IN2 Col4a1
## 54 1.132337e-09 0.1459152 0.233 0.115 2.313817e-05 IN2 Shank3
## 55 3.865699e-119 1.4448321 0.650 0.015 7.899170e-115 IN3 Sst
## 56 3.504370e-24 0.8038992 1.000 0.955 7.160830e-20 IN3 Nlgn1
## 57 5.170864e-10 0.2690654 0.243 0.051 1.056614e-05 IN3 Fat4
## 58 0.000000e+00 1.7350722 0.997 0.915 0.000000e+00 IPAN1 Fgf14
## 59 0.000000e+00 2.1177143 0.703 0.104 0.000000e+00 IPAN1 Nmu
## 60 0.000000e+00 1.1522530 0.821 0.170 0.000000e+00 IPAN1 Agrn
## 61 0.000000e+00 0.9299479 0.997 0.983 0.000000e+00 IPAN1 Fgf13
## 62 6.754990e-293 0.3905187 0.283 0.018 1.380315e-288 IPAN1 Il7
## 63 2.085811e-218 0.2727499 0.206 0.011 4.262146e-214 IPAN1 Bmp4
## 64 9.166759e-139 0.2058529 0.152 0.012 1.873136e-134 IPAN1 Vwf
## 65 6.484031e-95 0.4675030 0.785 0.668 1.324947e-90 IPAN1 App
## 66 2.852418e-90 0.3352033 0.426 0.203 5.828632e-86 IPAN1 Adam10
## 67 1.693378e-87 0.3077026 0.384 0.161 3.460248e-83 IPAN1 Nrg2
## 68 1.248249e-77 0.3125672 0.430 0.214 2.550673e-73 IPAN1 Gnb4
## 69 2.666037e-72 0.2014929 0.199 0.056 5.447781e-68 IPAN1 Anxa2
## 70 8.034726e-62 0.3295213 0.624 0.429 1.641816e-57 IPAN1 Gnas
## 71 3.786718e-46 0.1617267 0.185 0.067 7.737779e-42 IPAN1 S100a10
## 72 7.160418e-43 0.2623354 0.627 0.455 1.463160e-38 IPAN1 Rtn4
## 73 7.947266e-39 0.2092312 0.397 0.237 1.623944e-34 IPAN1 L1cam
## 74 2.048710e-30 0.2057777 0.315 0.195 4.186334e-26 IPAN1 Hsp90aa1
## 75 7.202046e-22 0.1161035 0.234 0.138 1.471666e-17 IPAN1 Hmgb1
## 76 1.318258e-20 0.1373151 0.285 0.180 2.693728e-16 IPAN1 Hspa8
## 77 9.104585e-19 0.1045110 0.194 0.111 1.860431e-14 IPAN1 Lgals8
## 78 0.000000e+00 1.4905029 0.761 0.025 0.000000e+00 IPAN2 Sema5a
## 79 3.849160e-280 0.7507954 0.523 0.108 7.865373e-276 IPAN2 Vcan
## 80 1.610701e-244 0.5654185 0.400 0.058 3.291307e-240 IPAN2 Serpine2
## 81 1.806332e-139 1.6640545 0.942 0.367 3.691058e-135 IPAN3 Gng2
## 82 5.388104e-19 0.4219427 0.192 0.015 1.101005e-14 IPAN3 Col8a1
## 83 8.395130e-14 0.2982836 0.217 0.040 1.715461e-09 IPAN3 Lrig1
## 84 7.181188e-13 0.2061423 0.167 0.018 1.467404e-08 IPAN3 Lamb1
## 85 2.830143e-10 0.2201720 0.192 0.034 5.783115e-06 IPAN3 Sema3c
## 86 0.000000e+00 2.7296055 0.980 0.119 0.000000e+00 IPAN4 Ntng1
## 87 0.000000e+00 1.4326698 0.703 0.004 0.000000e+00 IPAN4 Nxph2
## 88 3.700578e-49 0.3581029 0.324 0.105 7.561762e-45 IPAN4 Cd24a
## 89 7.195690e-44 0.3025856 0.279 0.078 1.470367e-39 IPAN4 Vegfamarkers.new_receptor.pct_0.15.padj_0.001## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## 1 0.000000e+00 0.5696470 0.758 0.502 0.000000e+00 EMN1 Ryr2
## 2 0.000000e+00 0.5207381 0.483 0.202 0.000000e+00 EMN1 Chrna7
## 3 0.000000e+00 0.4537600 0.360 0.093 0.000000e+00 EMN1 Oprk1
## 4 2.034240e-271 0.3136114 0.273 0.075 4.156767e-267 EMN1 Epha4
## 5 1.217599e-137 0.2181927 0.222 0.087 2.488041e-133 EMN1 Itga6
## 6 5.993268e-118 0.2926601 0.782 0.715 1.224664e-113 EMN1 Fgfr2
## 7 1.493752e-116 0.2386946 0.304 0.167 3.052332e-112 EMN1 Ephb2
## 8 1.429515e-211 0.5893812 0.973 0.745 2.921071e-207 EMN2 Epha6
## 9 5.527483e-66 0.1938802 0.212 0.072 1.129486e-61 EMN2 Ddr2
## 10 2.272633e-38 0.1892803 0.425 0.270 4.643898e-34 EMN2 Nrxn2
## 11 3.528621e-31 0.1790129 0.357 0.226 7.210384e-27 EMN2 Cd47
## 12 8.178191e-31 0.1372952 0.226 0.120 1.671132e-26 EMN2 Lgr4
## 13 8.390408e-22 0.1064386 0.617 0.499 1.714496e-17 EMN2 Lrrc4
## 14 0.000000e+00 1.3845582 0.894 0.286 0.000000e+00 EMN3 Sorcs1
## 15 0.000000e+00 1.0376244 0.918 0.541 0.000000e+00 EMN3 Lrp1b
## 16 1.025052e-82 0.2338149 0.219 0.067 2.094592e-78 EMN3 Agtr1a
## 17 1.424846e-193 1.0748133 0.710 0.107 2.911530e-189 EMN4 Ntrk2
## 18 1.429339e-59 0.3416694 0.264 0.027 2.920711e-55 EMN4 Itgb8
## 19 0.000000e+00 2.3913595 1.000 0.860 0.000000e+00 EMN5 Grm7
## 20 0.000000e+00 2.3653729 0.962 0.194 0.000000e+00 EMN5 Unc5d
## 21 1.309460e-196 0.6894317 0.449 0.012 2.675751e-192 EMN5 Egfr
## 22 1.299446e-133 0.9245781 0.753 0.192 2.655289e-129 EMN5 Ptprz1
## 23 1.479486e-104 0.8215978 0.788 0.298 3.023181e-100 EMN5 Nrp2
## 24 2.221920e-89 0.8346445 1.000 0.876 4.540272e-85 EMN5 Kcnq3
## 25 5.912632e-27 0.2898816 0.325 0.110 1.208187e-22 EMN5 Kcnj6
## 26 0.000000e+00 1.2098622 0.977 0.584 0.000000e+00 IMN1 Epha5
## 27 6.782722e-208 0.3996465 0.423 0.192 1.385981e-203 IMN1 Igf2r
## 28 7.483520e-171 0.3270215 0.354 0.154 1.529182e-166 IMN1 Oprd1
## 29 3.633610e-53 0.2895996 0.297 0.111 7.424918e-49 IMN2 Htr2c
## 30 4.599470e-28 0.2164714 0.427 0.260 9.398557e-24 IMN2 Cdon
## 31 2.637889e-272 1.4455445 0.990 0.552 5.390262e-268 IMN3 Alcam
## 32 3.983898e-180 1.2349436 0.966 0.583 8.140696e-176 IMN3 Lrrc4c
## 33 6.710387e-174 0.9432587 0.813 0.220 1.371200e-169 IMN3 Gfra1
## 34 2.782201e-166 0.6135130 0.412 0.029 5.685150e-162 IMN3 Sorcs2
## 35 2.584663e-131 0.7508023 0.557 0.116 5.281500e-127 IMN3 Sorcs3
## 36 6.686611e-77 0.6382527 0.881 0.586 1.366342e-72 IMN3 Cacna1c
## 37 1.446881e-55 0.4236405 0.457 0.153 2.956557e-51 IMN3 Itga8
## 38 2.434740e-52 0.4313000 0.586 0.260 4.975147e-48 IMN3 Ret
## 39 7.268367e-51 0.2674485 0.213 0.032 1.485218e-46 IMN3 Npy2r
## 40 2.409844e-33 0.2503877 0.266 0.079 4.924274e-29 IMN3 Tyro3
## 41 1.097179e-31 0.2214288 0.211 0.053 2.241976e-27 IMN3 Thy1
## 42 2.668710e-29 0.3000703 0.590 0.334 5.453242e-25 IMN3 Ptprj
## 43 1.395181e-21 0.2537882 0.421 0.221 2.850912e-17 IMN3 Plxnb1
## 44 2.886256e-21 0.2248048 0.292 0.126 5.897776e-17 IMN3 Fzd3
## 45 4.568093e-12 0.2013487 0.416 0.266 9.334442e-08 IMN3 Insr
## 46 5.477034e-12 0.1614192 0.308 0.175 1.119177e-07 IMN3 Adcyap1r1
## 47 3.132961e-08 0.1036473 0.163 0.082 6.401893e-04 IMN3 Sdc2
## 48 4.357805e-08 0.1033503 0.173 0.091 8.904738e-04 IMN3 Scarb1
## 49 2.742455e-98 0.8506552 0.997 0.887 5.603933e-94 IMN4 Cadm1
## 50 2.109198e-59 0.3971040 0.285 0.032 4.309936e-55 IMN4 Adcy8
## 51 6.066951e-51 0.2319856 0.173 0.008 1.239721e-46 IMN4 Tacr3
## 52 1.731136e-22 0.2449520 0.252 0.072 3.537402e-18 IMN4 Notch2
## 53 9.300447e-147 0.6288793 0.433 0.078 1.900453e-142 IN1 Epha8
## 54 1.869240e-30 0.2308660 0.285 0.113 3.819605e-26 IN1 Sort1
## 55 1.875902e-24 0.1618972 0.155 0.045 3.833219e-20 IN1 Ednrb
## 56 2.135338e-08 0.1248383 0.211 0.127 4.363350e-04 IN1 Tgfbr3
## 57 0.000000e+00 2.4042144 1.000 0.832 0.000000e+00 IN2 Kcnd2
## 58 4.012357e-163 0.9049220 0.642 0.105 8.198850e-159 IN2 Ptprm
## 59 4.318443e-155 0.5486427 0.374 0.011 8.824307e-151 IN2 Sctr
## 60 4.180491e-86 0.6461999 1.000 0.987 8.542415e-82 IN2 Nrxn1
## 61 9.501281e-79 0.2848264 0.212 0.008 1.941492e-74 IN2 Ntsr1
## 62 3.684699e-71 0.4492332 0.361 0.055 7.529314e-67 IN2 Rtn4rl1
## 63 1.019314e-70 0.4843119 0.382 0.064 2.082867e-66 IN2 Nrp1
## 64 2.378602e-58 0.3282018 0.249 0.027 4.860434e-54 IN2 Lingo1
## 65 5.694484e-18 0.2806563 0.456 0.244 1.163611e-13 IN2 Plscr4
## 66 2.873623e-13 0.2769928 0.711 0.529 5.871961e-09 IN2 Cntn1
## 67 4.242823e-243 2.6705191 1.000 0.361 8.669785e-239 IN3 Robo1
## 68 1.762096e-30 0.6008290 0.427 0.068 3.600666e-26 IN3 Nrcam
## 69 1.260777e-19 0.3588583 0.272 0.027 2.576272e-15 IN3 Vipr2
## 70 5.864233e-16 0.5714126 0.553 0.201 1.198297e-11 IN3 Gfra2
## 71 2.349976e-13 0.2554309 0.175 0.015 4.801941e-09 IN3 Tacr1
## 72 0.000000e+00 2.8285442 0.999 0.412 0.000000e+00 IPAN1 Nrxn3
## 73 0.000000e+00 2.4257390 0.976 0.202 0.000000e+00 IPAN1 Robo2
## 74 0.000000e+00 2.3298186 0.989 0.187 0.000000e+00 IPAN1 Ntrk3
## 75 0.000000e+00 1.7609063 0.946 0.204 0.000000e+00 IPAN1 Plxna4
## 76 0.000000e+00 1.7063221 0.916 0.156 0.000000e+00 IPAN1 Ccbe1
## 77 0.000000e+00 0.8990972 0.777 0.326 0.000000e+00 IPAN1 Cnr1
## 78 0.000000e+00 1.1113411 0.590 0.009 0.000000e+00 IPAN1 Itgb6
## 79 0.000000e+00 0.7768492 0.553 0.044 0.000000e+00 IPAN1 Galr1
## 80 0.000000e+00 0.6517939 0.486 0.096 0.000000e+00 IPAN1 Ngfr
## 81 0.000000e+00 0.5432981 0.373 0.020 0.000000e+00 IPAN1 Met
## 82 4.699070e-282 0.7838060 0.834 0.547 9.602079e-278 IPAN1 Igf1r
## 83 1.950625e-191 0.5055353 0.401 0.120 3.985907e-187 IPAN1 Dysf
## 84 2.664584e-189 0.5616217 0.554 0.199 5.444812e-185 IPAN1 Calcrl
## 85 8.785513e-189 0.3792247 0.309 0.052 1.795232e-184 IPAN1 Ptger3
## 86 1.052026e-186 0.4668295 0.481 0.164 2.149710e-182 IPAN1 Npr2
## 87 1.563327e-160 0.2500400 0.197 0.020 3.194502e-156 IPAN1 Gcgr
## 88 7.165252e-122 0.2060210 0.161 0.018 1.464148e-117 IPAN1 Itga2
## 89 5.557332e-119 0.2072282 0.172 0.022 1.135585e-114 IPAN1 Unc5b
## 90 1.507779e-112 0.4181966 0.611 0.329 3.080996e-108 IPAN1 Scn5a
## 91 8.811416e-97 0.2263668 0.199 0.041 1.800525e-92 IPAN1 Il13ra1
## 92 4.741757e-86 0.3659477 0.514 0.284 9.689307e-82 IPAN1 Cd9
## 93 5.826719e-67 0.3080774 0.545 0.324 1.190632e-62 IPAN1 Ptprs
## 94 2.064858e-59 0.1627802 0.162 0.044 4.219330e-55 IPAN1 Cntfr
## 95 7.680874e-58 0.2348555 0.284 0.124 1.569510e-53 IPAN1 Cd81
## 96 1.329464e-52 0.2785298 0.393 0.213 2.716626e-48 IPAN1 Plxna2
## 97 2.490239e-47 0.2101567 0.311 0.153 5.088554e-43 IPAN1 Ptger4
## 98 1.907800e-41 0.2002669 0.356 0.198 3.898399e-37 IPAN1 Bmpr1a
## 99 1.478166e-37 0.2257388 0.403 0.250 3.020485e-33 IPAN1 Aplp2
## 100 7.062835e-31 0.1257900 0.168 0.073 1.443220e-26 IPAN1 Plxna3
## 101 2.806665e-29 0.1540963 0.268 0.148 5.735139e-25 IPAN1 Tspan17
## 102 3.211133e-29 0.1894583 0.404 0.263 6.561629e-25 IPAN1 Trpc6
## 103 1.271821e-26 0.1611509 0.311 0.188 2.598840e-22 IPAN1 Itgb1
## 104 5.474453e-26 0.1285295 0.208 0.108 1.118650e-21 IPAN1 Acvr1
## 105 9.130241e-24 0.1838976 0.434 0.309 1.865673e-19 IPAN1 Bmpr2
## 106 1.183126e-20 0.1330098 0.243 0.145 2.417599e-16 IPAN1 Ldlr
## 107 3.977896e-18 0.1003230 0.155 0.082 8.128433e-14 IPAN1 Lamp1
## 108 1.213296e-16 0.1025542 0.212 0.130 2.479249e-12 IPAN1 Lrp6
## 109 9.670794e-15 0.1142633 0.227 0.147 1.976130e-10 IPAN1 Tmem126a
## 110 0.000000e+00 1.2980715 0.990 0.654 0.000000e+00 IPAN2 Alk
## 111 5.540486e-287 1.0622072 0.430 0.071 1.132143e-282 IPAN2 Dcc
## 112 3.557226e-233 0.3622293 0.252 0.012 7.268835e-229 IPAN2 Nmur2
## 113 7.634807e-194 0.7217496 0.385 0.087 1.560096e-189 IPAN2 Ghr
## 114 1.085581e-153 0.3653681 0.309 0.052 2.218276e-149 IPAN2 Nectin3
## 115 8.583972e-115 0.4441081 0.516 0.239 1.754049e-110 IPAN2 Sorl1
## 116 6.777704e-68 0.2472605 0.251 0.076 1.384956e-63 IPAN2 Atp1a3
## 117 5.674870e-32 0.1841310 0.236 0.109 1.159603e-27 IPAN2 Traf3
## 118 1.518691e-15 0.1240995 0.227 0.135 3.103293e-11 IPAN2 Slc22a17
## 119 4.709349e-80 1.7925798 1.000 0.695 9.623083e-76 IPAN3 Gpc6
## 120 1.622181e-27 0.7533781 0.642 0.259 3.314765e-23 IPAN3 Oprm1
## 121 6.601298e-25 0.6926799 0.358 0.142 1.348909e-20 IPAN3 Epha7
## 122 9.049737e-23 0.3966274 0.217 0.016 1.849223e-18 IPAN3 Ror1
## 123 2.268212e-09 0.3030330 0.292 0.109 4.634865e-05 IPAN3 Ramp1
## 124 0.000000e+00 1.1795053 0.629 0.014 0.000000e+00 IPAN4 Bmpr1b
## 125 6.504642e-237 0.9028330 0.483 0.025 1.329159e-232 IPAN4 Flrt2
## 126 4.599746e-119 0.9167111 0.921 0.557 9.399121e-115 IPAN4 Unc5c
## 127 5.351041e-114 0.4655202 0.277 0.019 1.093432e-109 IPAN4 Ephb1
## 128 2.367353e-88 0.2369190 0.157 0.003 4.837450e-84 IPAN4 Npy5r
## 129 6.581974e-72 0.2937324 0.191 0.015 1.344960e-67 IPAN4 Cckar
## 130 1.043326e-67 0.2705104 0.181 0.014 2.131933e-63 IPAN4 Glp1r
## 131 1.590873e-63 0.3124106 0.230 0.036 3.250790e-59 IPAN4 Npy1r
## 132 1.283549e-21 0.2569076 0.318 0.151 2.622804e-17 IPAN4 Fgfr1
## 133 5.743617e-13 0.1744222 0.244 0.123 1.173651e-08 IPAN4 Slc16a7
## 134 2.051228e-11 0.1338159 0.196 0.093 4.191479e-07 IPAN4 Tnfrsf21PBSvsINF
DotPlot(test1.seur, features = markers.new_ligand.pct_0.15.padj_0.001$gene, group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 10.15))+ scale_y_discrete(limits=rev) + labs(title="ligand.pct_0.15.padj_0.001") +
scale_color_gradientn(colours = material.heat(100))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
DotPlot(test1.seur, features = markers.new_receptor.pct_0.15.padj_0.001$gene, group.by = "intAnno1",
cols = c("midnightblue","darkorange1")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 10))+ scale_y_discrete(limits=rev) + labs(title="receptor.pct_0.15.padj_0.001") +
scale_color_gradientn(colours = material.heat(100))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
NatNeur2021
DotPlot(ref.seur, features = markers.new_ligand.pct_0.15.padj_0.001$gene, group.by = "Anno2",
cols = c("midnightblue","darkorange1")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 10.15))+ scale_y_discrete(limits=rev) + labs(title="ligand.pct_0.15.padj_0.001") +
scale_color_gradientn(colours = material.heat(100))## Warning in FetchData.Seurat(object = object, vars = features, cells = cells):
## The following requested variables were not found: Afdn## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
DotPlot(ref.seur, features = markers.new_receptor.pct_0.15.padj_0.001$gene, group.by = "Anno2",
cols = c("midnightblue","darkorange1")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1,size = 10))+ scale_y_discrete(limits=rev) + labs(title="receptor.pct_0.15.padj_0.001") +
scale_color_gradientn(colours = material.heat(100))## Warning in FetchData.Seurat(object = object, vars = features, cells = cells):
## The following requested variables were not found: Nectin3## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.